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'MR99' ** Obsolete normative reference: RFC 1349 (Obsoleted by RFC 2474) ** Downref: Normative reference to an Informational RFC: RFC 1458 ** Obsolete normative reference: RFC 1771 (Obsoleted by RFC 4271) ** Downref: Normative reference to an Informational RFC: RFC 1992 ** Downref: Normative reference to an Informational RFC: RFC 1998 ** Obsolete normative reference: RFC 2178 (Obsoleted by RFC 2328) ** Downref: Normative reference to an Informational RFC: RFC 2216 ** Downref: Normative reference to an Informational RFC: RFC 2330 ** Downref: Normative reference to an Informational RFC: RFC 2386 ** Downref: Normative reference to an Informational RFC: RFC 2475 ** Obsolete normative reference: RFC 2679 (Obsoleted by RFC 7679) ** Obsolete normative reference: RFC 2680 (Obsoleted by RFC 7680) ** Downref: Normative reference to an Informational RFC: RFC 2702 ** Downref: Normative reference to an Informational RFC: RFC 2722 ** Downref: Normative reference to an Informational RFC: RFC 2753 ** Downref: Normative reference to an Informational RFC: RFC 2998 -- Possible downref: Non-RFC (?) normative reference: ref. 'SHAR' -- Possible downref: Non-RFC (?) normative reference: ref. 'SLDC98' -- Possible downref: Non-RFC (?) normative reference: ref. 'SMIT' -- Possible downref: Non-RFC (?) normative reference: ref. 'XIAO' -- Possible downref: Non-RFC (?) normative reference: ref. 'YARE95' Summary: 21 errors (**), 0 flaws (~~), 31 warnings (==), 33 comments (--). Run idnits with the --verbose option for more detailed information about the items above. -------------------------------------------------------------------------------- 2 Internet Engineering Task Force 3 INTERNET-DRAFT 4 TE Working Group 5 Daniel O. Awduche 6 Expiration Date: November 2001 Movaz Networks 8 Angela Chiu 9 Celion Networks 11 Anwar Elwalid 12 Lucent Technologies 14 Indra Widjaja 15 Lucent Technologies 17 XiPeng Xiao 18 Photuris 20 A Framework for Internet Traffic Engineering 22 draft-ietf-tewg-framework-05.txt 24 Status of this Memo 26 This document is an Internet-Draft and is in full conformance with 27 all provisions of Section 10 of RFC2026. 29 Internet-Drafts are working documents of the Internet Engineering 30 Task Force (IETF), its areas, and its working groups. Note that 31 other groups may also distribute working documents as Internet- 32 Drafts. 34 Internet-Drafts are draft documents valid for a maximum of six months 35 and may be updated, replaced, or obsoleted by other documents at any 36 time. It is inappropriate to use Internet-Drafts as reference 37 material or to cite them other than as "work in progress." 39 The list of current Internet-Drafts can be accessed at 40 http://www.ietf.org/1id-abstracts.html 42 The list of Internet-Draft Shadow Directories can be accessed at 43 http://www.ietf.org/shadow.html. 45 Abstract 47 This memo describes a framework for Traffic Engineering (TE) in the 48 Internet. The framework is intended to promote better understanding 49 of the issues surrounding traffic engineering in IP networks, and to 50 provide a common basis for the development of traffic engineering 51 capabilities for the Internet. The principles, architectures, and 52 methodologies for performance evaluation and performance optimization 53 of operational IP networks are discussed throughout this document. 54 The optimization goals of traffic engineering are to enhance the 55 performance of IP traffic while utilizing network resources 56 economically and reliably. The framework includes a set of generic 57 recommendations, and options for Internet traffic engineering. The 58 framework can serve as a guide to implementors of online and offline 59 Internet traffic engineering mechanisms, tools, and support systems. 60 The framework can also help service providers devise traffic 61 engineering solutions for their networks. 63 Table of Contents 65 1.0 Introduction...................................................3 66 1.1 What is Internet Traffic Engineering?.......................4 67 1.2 Scope.......................................................7 68 1.3 Terminology.................................................8 69 2.0 Background....................................................11 70 2.1 Context of Internet Traffic Engineering....................11 71 2.2 Network Context............................................12 72 2.3 Problem Context............................................14 73 2.3.1 Congestion and its Ramifications......................15 74 2.4 Solution Context...........................................15 75 2.4.1 Combating the Congestion Problem......................17 76 2.5 Implementation and Operational Context.....................19 77 3.0 Traffic Engineering Process Model.............................20 78 3.1 Components of the Traffic Engineering Process Model........21 79 3.2 Measurement................................................21 80 3.3 Modeling, Analysis, and Simulation.........................22 81 3.4 Optimization...............................................23 82 4.0 Historical Review and Recent Developments.....................24 83 4.1 Traffic Engineering in Classical Telephone Networks........24 84 4.2 Evolution of Traffic Engineering in the Internet...........26 85 4.2.1 Adaptive Routing in ARPANET...........................26 86 4.2.2 Dynamic Routing in the Internet.......................27 87 4.2.3 ToS Routing...........................................27 88 4.2.4 Equal Cost Multi-Path.................................28 89 4.2.5 Nimrod................................................28 90 4.3 Overlay Model..............................................29 91 4.4 Constraint-Based Routing...................................29 92 4.5 Overview of Other IETF Projects Related to Traffic 93 Engineering................................................30 94 4.5.1 Integrated Services...................................30 95 4.5.2 RSVP..................................................31 96 4.5.3 Differentiated Services...............................32 97 4.5.4 MPLS..................................................33 98 4.5.5 IP Performance Metrics................................34 99 4.5.6 Flow Measurement......................................34 100 4.5.7 Endpoint Congestion Management........................35 101 4.6 Overview of ITU Activities Related to Traffic 102 Engineering................................................35 103 4.7 Content Distribution.......................................36 104 5.0 Taxonomy of Traffic Engineering Systems.......................37 105 5.1 Time-Dependent Versus State-Dependent......................37 106 5.2 Offline Versus Online......................................38 107 5.3 Centralized Versus Distributed.............................38 108 5.4 Local Versus Global........................................39 109 5.5 Prescriptive Versus Descriptive............................39 110 5.6 Open-Loop Versus Closed-Loop...............................40 111 5.7 Tactical vs Strategic......................................40 112 6.0 Recommendations for Internet Traffic Engineering..............40 113 6.1 Generic Non-functional Recommendations.....................41 114 6.2 Routing Recommendations....................................42 115 6.3 Traffic Mapping Recommendations............................45 116 6.4 Measurement Recommendations................................45 117 6.5 Network Survivability......................................46 118 6.5.1 Survivability in MPLS Based Networks..................48 119 6.5.2 Protection Option.....................................49 120 6.6 Traffic Engineering in Diffserv Environments...............50 121 6.7 Network Controllability....................................52 122 7.0 Inter-Domain Considerations...................................52 123 8.0 Overview of Contemporary TE Practices in Operational 124 IP Networks...................................................53 125 9.0 Conclusion....................................................55 126 10.0 Security Considerations......................................55 127 11.0 Acknowledgments..............................................55 128 12.0 References...................................................56 129 13.0 Authors' Addresses...........................................60 131 1.0 Introduction 133 This memo describes a framework for Internet traffic engineering. 134 The objective of the document is to articulate the general issues and 135 principles for Internet traffic engineering; and where appropriate to 136 provide recommendations, guidelines, and options for the development 137 of online and offline Internet traffic engineering capabilities and 138 support systems. 140 The framework can aid service providers in devising and implementing 141 traffic engineering solutions for their networks. Networking hardware 142 and software vendors will also find the framework helpful in the 143 development of mechanisms and support systems for the Internet 144 environment that support the traffic engineering function. 146 The framework provides a terminology for describing and understanding 147 common Internet traffic engineering concepts. The framework also 148 provides a taxonomy of known traffic engineering styles. In this 149 context, a traffic engineering style abstracts important aspects from 150 a traffic engineering methodology. Traffic engineering styles can be 151 viewed in different ways depending upon the specific context in which 152 they are used and the specific purpose which they serve. The 153 combination of styles and views results in a natural taxonomy of 154 traffic engineering systems. 156 Even though Internet traffic engineering is most effective when 157 applied end-to-end, the initial focus of this framework document is 158 intra-domain traffic engineering (that is, traffic engineering within 159 a given autonomous system). However, because a preponderance of 160 Internet traffic tends to be inter-domain (originating in one 161 autonomous system and terminating in another), this document provides 162 an overview of aspects pertaining to inter-domain traffic 163 engineering. 165 The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", 166 "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this 167 document are to be interpreted as described in RFC 2119. 169 1.1. What is Internet Traffic Engineering? 171 Internet traffic engineering is defined as that aspect of Internet 172 network engineering dealing with the issue of performance evaluation 173 and performance optimization of operational IP networks. Traffic 174 Engineering encompasses the application of technology and scientific 175 principles to the measurement, characterization, modeling, and 176 control of Internet traffic [RFC-2702, AWD2]. 178 Enhancing the performance of an operational network, at both the 179 traffic and resource levels, are major objectives of Internet traffic 180 engineering. This is accomplished by addressing traffic oriented 181 performance requirements, while utilizing network resources 182 economically and reliably. Traffic oriented performance measures 183 include delay, delay variation, packet loss, and throughput. 185 An important objective of Internet traffic engineering is to 186 facilitate reliable network operations [RFC-2702]. Reliable network 187 operations can be facilitated by providing mechanisms that enhance 188 network integrity and by embracing policies emphasizing network 189 survivability. This results in a minimization of the vulnerability of 190 the network to service outages arising from errors, faults, and 191 failures occurring within the infrastructure. 193 The Internet exists in order to transfer information from source 194 nodes to destination nodes. Accordingly, one of the most significant 195 functions performed by the Internet is the routing of traffic from 196 ingress nodes to egress nodes. Therefore, one of the most distinctive 197 functions performed by Internet traffic engineering is the control 198 and optimization of the routing function, to steer traffic through 199 the network in the most effective way. 201 Ultimately, it is the performance of the network as seen by end users 202 of network services that is truly paramount. This crucial point 203 should be considered throughout the development of traffic 204 engineering mechanisms and policies. The characteristics visible to 205 end users are the emergent properties of the network, which are the 206 characteristics of the network when viewed as a whole. A central goal 207 of the service provider, therefore, is to enhance the emergent 208 properties of the network while taking economic considerations into 209 account. 211 The importance of the above observation regarding the emergent 212 properties of networks is that special care must be taken when 213 choosing network performance measures to optimize. Optimizing the 214 wrong measures may achieve certain local objectives, but may have 215 disastrous consequences on the emergent properties of the network and 216 thereby on the quality of service perceived by end-users of network 217 services. 219 A subtle, but practical advantage of the systematic application of 220 traffic engineering concepts to operational networks is that it helps 221 to identify and structure goals and priorities in terms of enhancing 222 the quality of service delivered to end-users of network services. 223 The application of traffic engineering concepts also aids in the 224 measurement and analysis of the achievement of these goals. 226 The optimization aspects of traffic engineering can be achieved 227 through capacity management and traffic management. As used in this 228 document, capacity management includes capacity planning, routing 229 control, and resource management. Network resources of particular 230 interest include link bandwidth, buffer space, and computational 231 resources. Likewise, as used in this document, traffic management 232 includes (1) nodal traffic control functions such as traffic 233 conditioning, queue management, scheduling, and (2) other functions 234 that regulate traffic flow through the network or that arbitrate 235 access to network resources between different packets or between 236 different traffic streams. 238 The optimization objectives of Internet traffic engineering should be 239 viewed as a continual and iterative process of network performance 240 improvement and not simply as a one time goal. Traffic engineering 241 also demands continual development of new technologies and new 242 methodologies for network performance enhancement. 244 The optimization objectives of Internet traffic engineering may 245 change over time as new requirements are imposed, as new technologies 246 emerge, or as new insights are brought to bear on the underlying 247 problems. Moreover, different networks may have different 248 optimization objectives, depending upon their business models, 249 capabilities, and operating constraints. The optimization aspects of 250 traffic engineering are ultimately concerned with network control 251 regardless of the specific optimization goals in any particular 252 environment. 254 Thus, the optimization aspects of traffic engineering can be viewed 255 from a control perspective. The aspect of control within the Internet 256 traffic engineering arena can be pro-active and/or reactive. In the 257 pro-active case, the traffic engineering control system takes 258 preventive action to obviate predicted unfavorable future network 259 states. It may also take perfective action to induce a more 260 desirable state in the future. In the reactive case, the control 261 system responds correctively and perhaps adaptively to events that 262 have already transpired in the network. 264 The control dimension of Internet traffic engineering responds at 265 multiple levels of temporal resolution to network events. Certain 266 aspects of capacity management, such as capacity planning, respond at 267 very coarse temporal levels, ranging from days to possibly years. The 268 introduction of automatically switched optical transport networks 269 (e.g. based on the Multi-protocol Lambda Switching concepts) could 270 significantly reduce the lifecycle for capacity planning by 271 expediting provisioning of optical bandwidth. Routing control 272 functions operate at intermediate levels of temporal resolution, 273 ranging from milliseconds to days. Finally, the packet level 274 processing functions (e.g. rate shaping, queue management, and 275 scheduling) operate at very fine levels of temporal resolution, 276 ranging from picoseconds to milliseconds while responding to the 277 real-time statistical behavior of traffic. The subsystems of Internet 278 traffic engineering control include: capacity augmentation, routing 279 control, traffic control, and resource control (including control of 280 service policies at network elements). When capacity is to be 281 augmented for tactical purposes, it may be desirable to devise a 282 deployment plan that expedites bandwidth provisioning while 283 minimizing installation costs. 285 Inputs into the traffic engineering control system include network 286 state variables, policy variables, and decision variables. 288 One major challenge of Internet traffic engineering is the 289 realization of automated control capabilities that adapt quickly and 290 cost effectively to significant changes in a network's state, while 291 still maintaining stability. 293 Another critical dimension of Internet traffic engineering is network 294 performance evaluation, which is important for assessing the 295 effectiveness of traffic engineering methods, and for monitoring and 296 verifying compliance with network performance goals. Results from 297 performance evaluation can be used to identify existing problems, 298 guide network re-optimization, and aid in the prediction of potential 299 future problems. 301 Performance evaluation can be achieved in many different ways. The 302 most notable techniques include analytical methods, simulation, and 303 empirical methods based on measurements. When analytical methods or 304 simulation are used, network nodes and links can be modeled to 305 capture relevant operational features such as topology, bandwidth, 306 buffer space, and nodal service policies (link scheduling, packet 307 prioritization, buffer management, etc). Analytical traffic models 308 can be used to depict dynamic and behavioral traffic characteristics, 309 such as burstiness, statistical distributions, and dependence. 311 Performance evaluation can be quite complicated in practical network 312 contexts. A number of techniques can be used to simplify the 313 analysis, such as abstraction, decomposition, and approximation. For 314 example, simplifying concepts such as effective bandwidth and 315 effective buffer [Elwalid] may be used to approximate nodal behaviors 316 at the packet level and simplify the analysis at the connection 317 level. Network analysis techniques using, for example, queuing models 318 and approximation schemes based on asymptotic and decomposition 319 techniques can render the analysis even more tractable. In 320 particular, an emerging set of concepts known as network calculus 321 [CRUZ] based on deterministic bounds may simplify network analysis 322 relative to classical stochastic techniques. When using analytical 323 techniques, care should be taken to ensure that the models faithfully 324 reflect the relevant operational characteristics of the modeled 325 network entities. 327 Simulation can be used to evaluate network performance or to verify 328 and validate analytical approximations. Simulation can, however, be 329 computationally costly and may not always provide sufficient 330 insights. An appropriate approach to a given network performance 331 evaluation problem may involve a hybrid combination of analytical 332 techniques, simulation, and empirical methods. 334 As a general rule, traffic engineering concepts and mechanisms must 335 be sufficiently specific and well defined to address known 336 requirements, but simultaneously flexible and extensible to 337 accommodate unforeseen future demands. 339 1.2. Scope 341 The scope of this document is intra-domain traffic engineering; that 342 is, traffic engineering within a given autonomous system in the 343 Internet. The framework will discuss concepts pertaining to intra- 344 domain traffic control, including such issues as routing control, 345 micro and macro resource allocation, and the control coordination 346 problems that arise consequently. 348 This document will describe and characterize techniques already in 349 use or in advanced development for Internet traffic engineering. The 350 way these techniques fit together will be discussed and scenarios in 351 which they are useful will be identified. 353 Although the emphasis is on intra-domain traffic engineering, in 354 Section 7.0, an overview of the high level considerations pertaining 355 to inter-domain traffic engineering will be provided. inter-domain 356 Internet traffic engineering is crucial to the performance 357 enhancement of the global Internet infrastructure. 359 Whenever possible, relevant requirements from existing IETF documents 360 and other sources will be incorporated by reference. 362 1.3 Terminology 364 This subsection provides terminology which is useful for Internet 365 traffic engineering. The definitions presented apply to this 366 framework document. These terms may have other meanings elsewhere. 368 - Baseline analysis: 369 A study conducted to serve as a baseline for comparison to the 370 actual behavior of the network. 372 - Busy hour: 373 A one hour period within a specified interval of time 374 (typically 24 hours) in which the traffic load in a 375 network or sub-network is greatest. 377 - Bottleneck 378 A network element whose input traffic rate tends to be greater 379 than its output rate. 381 - Congestion: 382 A state of a network resource in which the traffic incident 383 on the resource exceeds its output capacity over an interval 384 of time. 386 - Congestion avoidance: 387 An approach to congestion management that attempts to obviate 388 the occurrence of congestion. 390 - Congestion control: 391 An approach to congestion management that attempts to remedy 392 congestion problems that have already occurred. 394 - Constraint-based routing: 395 A class of routing protocols that take specified traffic 396 attributes, network constraints, and policy constraints into 397 account when making routing decisions. Constraint-based 398 routing is applicable to traffic aggregates as well as flows. 399 It is a generalization of QoS routing. 401 - Demand side congestion management: 402 A congestion management scheme that addresses congestion 403 problems by regulating or conditioning offered load. 405 - Effective bandwidth: 406 The minimum amount of bandwidth that can be assigned to a flow 407 or traffic aggregate in order to deliver 'acceptable service 408 quality' to the flow or traffic aggregate. 410 - Egress traffic: 411 Traffic exiting a network or network element. 413 - Hot-spot 414 A network element or subsystem which is in a state of 415 congestion. 417 - Ingress traffic: 418 Traffic entering a network or network element. 420 - Inter-domain traffic: 421 Traffic that originates in one Autonomous system and 422 terminates in another. 424 - Loss network: 425 A network that does not provide adequate buffering for 426 traffic, so that traffic entering a busy resource within 427 the network will be dropped rather than queued. 429 - Metric: 430 A parameter defined in terms of standard units of 431 measurement. 433 - Measurement Methodology: 434 A repeatable measurement technique used to derive one or 435 more metrics of interest. 437 - Network Survivability: 438 The capability to provide a prescribed level of QoS for 439 existing services after a given number of failures occur 440 within the network. 442 - Offline traffic engineering: 443 A traffic engineering system that exists outside of the 444 network. 446 - Online traffic engineering: 447 A traffic engineering system that exists within the network, 448 typically implemented on or as adjuncts to operational network 449 elements. 451 - Performance measures: 452 Metrics that provide quantitative or qualitative measures of 453 the performance of systems or subsystems of interest. 455 - Performance management: 456 A systematic approach to improving effectiveness in the 457 accomplishment of specific networking goals related to 458 performance improvement. 460 - Performance Metric: 461 A performance parameter defined in terms of standard units of 462 measurement. 464 - Provisioning: 465 The process of assigning or configuring network resources to 466 meet certain requests. 468 - QoS routing: 470 Class of routing systems that selects paths to be used by a 471 flow based on the QoS requirements of the flow. 473 - Service Level Agreement: 474 A contract between a provider and a customer that guarantees 475 specific levels of performance and reliability at a certain 476 cost. 478 - Stability: 479 An operational state in which a network does not oscillate 480 in a disruptive manner from one mode to another mode. 482 - Supply side congestion management: 483 A congestion management scheme that provisions additional 484 network resources to address existing and/or anticipated 485 congestion problems. 487 - Transit traffic: 488 Traffic whose origin and destination are both outside of 489 the network under consideration. 491 - Traffic characteristic: 492 A description of the temporal behavior or a description of the 493 attributes of a given traffic flow or traffic aggregate. 495 - Traffic engineering system 496 A collection of objects, mechanisms, and protocols that are 497 used conjunctively to accomplish traffic engineering 498 objectives. 500 - Traffic flow: 501 A stream of packets between two end-points that can be 502 characterized in a certain way. A micro-flow has a more 503 specific definition: A micro-flow is a stream of packets with 504 a bounded inter-arrival time and with the same source and 505 destination addresses, source and destination ports, and 506 protocol ID. 508 - Traffic intensity: 509 A measure of traffic loading with respect to a resource 510 capacity over a specified period of time. In classical 511 telephony systems, traffic intensity is measured in units of 512 Erlang. 514 - Traffic matrix: 515 A representation of the traffic demand between a set of origin 516 and destination abstract nodes. An abstract node can consist 517 of one or more network elements. 519 - Traffic monitoring: 521 The process of observing traffic characteristics at a given 522 point in a network and collecting the traffic information for 523 analysis and further action. 525 - Traffic trunk: 526 An aggregation of traffic flows belonging to the same class 527 which are forwarded through a common path. A traffic trunk 528 may be characterized by an ingress and egress node, and a 529 set of attributes which determine its behavioral 530 characteristics and requirements from the network. 532 2.0 Background 534 The Internet has quickly evolved into a very critical communications 535 infrastructure, supporting significant economic, educational, and 536 social activities. Simultaneously, the delivery of Internet 537 communications services has become very competitive and end-users are 538 demanding very high quality service from their service providers. 539 Consequently, performance optimization of large scale IP networks, 540 especially public Internet backbones, has become an important 541 problem. Network performance requirements are multi-dimensional, 542 complex, and sometimes contradictory; making the traffic engineering 543 problem very challenging. 545 The network must convey IP packets from ingress nodes to egress nodes 546 efficiently, expeditiously and economically. Furthermore, in a 547 multiclass service environment (e.g. Diffserv capable networks), the 548 resource sharing parameters of the network must be appropriately 549 determined and configured according to prevailing policies and 550 service models to resolve resource contention issues arising from 551 mutual interference between packets traversing through the network. 552 Thus, consideration must be given to resolving competition for 553 network resources between traffic streams belonging to the same 554 service class (intra-class contention resolution) and traffic streams 555 belonging to different classes (inter-class contention resolution). 557 2.1 Context of Internet Traffic Engineering 559 The context of Internet traffic engineering pertains to the scenarios 560 where traffic engineering is used. A traffic engineering methodology 561 establishes appropriate rules to resolve traffic performance issues 562 occurring in a specific context. The context of Internet traffic 563 engineering includes: 565 (1) A network context defining the universe of discourse, 566 and in particular the situations in which the traffic 567 engineering problems occur. The network context 568 includes network structure, network policies, network 569 characteristics, network constraints, network quality 570 attributes, and network optimization criteria. 572 (2) A problem context defining the general and concrete 573 issues that traffic engineering addresses. The problem 574 context includes identification, abstraction of relevant 575 features, representation, formulation, specification of 576 the requirements on the solution space, and specification 577 of the desirable features of acceptable solutions. 579 (3) A solution context suggesting how to address the issues 580 identified by the problem context. The solution context 581 includes analysis, evaluation of alternatives, 582 prescription, and resolution. 584 (4) An implementation and operational context in which the 585 solutions are methodologically instantiated. The 586 implementation and operational context includes 587 planning, organization, and execution. 589 The context of Internet traffic engineering and the different problem 590 scenarios are discussed in the following subsections. 592 2.2 Network Context 594 IP networks range in size from small clusters of routers situated 595 within a given location, to thousands of interconnected routers, 596 switches, and other components distributed all over the world. 598 Conceptually, at the most basic level of abstraction, an IP network 599 can be represented as a distributed dynamical system consisting of: 600 (1) a set of interconnected resources which provide transport 601 services for IP traffic subject to certain constraints, (2) a demand 602 system representing the offered load to be transported through the 603 network, and (3) a response system consisting of network processes, 604 protocols, and related mechanisms which facilitate the movement of 605 traffic through the network [see also AWD2]. 607 The network elements and resources may have specific characteristics 608 restricting the manner in which the demand is handled. Additionally, 609 network resources may be equipped with traffic control mechanisms 610 superintending the way in which the demand is serviced. Traffic 611 control mechanisms may, for example, be used to control various 612 packet processing activities within a given resource, arbitrate 613 contention for access to the resource by different packets, and 614 regulate traffic behavior through the resource. A configuration 615 management and provisioning system may allow the settings of the 616 traffic control mechanisms to be manipulated by external or internal 617 entities in order to exercise control over the way in which the 618 network elements respond to internal and external stimuli. 620 The details of how the network provides transport services for 621 packets are specified in the policies of the network administrators 622 and are installed through network configuration management and policy 623 based provisioning systems. Generally, the types of services 624 provided by the network also depends upon the technology and 625 characteristics of the network elements and protocols, the prevailing 626 service and utility models, and the ability of the network 627 administrators to translate policies into network configurations. 629 Contemporary Internet networks have three significant 630 characteristics: (1) they provide real-time services, (2) they have 631 become mission critical, and (3) their operating environments are 632 very dynamic. The dynamic characteristics of IP networks can be 633 attributed in part to fluctuations in demand, to the interaction 634 between various network protocols and processes, to the rapid 635 evolution of the infrastructure which demands the constant inclusion 636 of new technologies and new network elements, and to transient and 637 persistent impairments which occur within the system. 639 Packets contend for the use of network resources as they are conveyed 640 through the network. A network resource is considered to be 641 congested if the arrival rate of packets exceed the output capacity 642 of the resource over an interval of time. Congestion may result in 643 some of the arrival packets being delayed or even dropped. 644 Congestion increases transit delays, delay variation, packet loss, 645 and reduces the predictability of network services. Clearly, 646 congestion is a highly undesirable phenomenon. 648 Combating congestion at reasonable cost is a major objective of 649 Internet traffic engineering. 651 Efficient sharing of network resources by multiple traffic streams is 652 a basic economic premise for packet switched networks in general and 653 the Internet in particular. A fundamental challenge in network 654 operation, especially in a large scale public IP network, is to 655 increase the efficiency of resource utilization while minimizing the 656 possibility of congestion. 658 Increasingly, the Internet will have to function in the presence of 659 different classes of traffic with different service requirements. The 660 advent of Differentiated Services [RFC 2475] makes this requirement 661 particularly acute. Thus, packets may be grouped into behavior 662 aggregates such that each behavior aggregate may have a common set of 663 behavioral characteristics or a common set of delivery requirements. 664 In practice, the delivery requirements of a specific set of packets 665 may be specified explicitly or implicitly. Two of the most important 666 traffic delivery requirements are capacity constraints and QoS 667 constraints. 669 Capacity constraints can be expressed statistically as peak rates, 670 mean rates, burst sizes, or as some deterministic notion of effective 671 bandwidth. QoS requirements can be expressed in terms of (1) 672 integrity constraints such as packet loss and (2) in terms of 673 temporal constraints such as timing restrictions for the delivery of 674 each packet (delay) and timing restrictions for the delivery of 675 consecutive packets belonging to the same traffic stream (delay 676 variation). 678 2.3 Problem Context 680 Fundamental problems exist in association with the operation of a 681 network described by the simple model of the previous subsection. 682 This subsection reviews the problem context in relation to the 683 traffic engineering function. 685 The identification, abstraction, representation, and measurement of 686 network features relevant to traffic engineering is a significant 687 issue. 689 One particularly important class of problems concerns how to 690 explicitly formulate the problems that traffic engineering attempts 691 to solve, how to identify the requirements on the solution space, how 692 to specify the desirable features of good solutions, how to actually 693 solve the problems, and how to measure and characterize the 694 effectiveness of the solutions. 696 Another class of problems concerns how to measure and estimate 697 relevant network state parameters. Effective traffic engineering 698 relies on a good estimate of the offered traffic load as well as a 699 view of the underlying topology and associated resource constraints. 700 A network-wide view of the topology is also a must for offline 701 planning. 703 Still another class of problems concerns how to characterize the 704 state of the network and how to evaluate its performance under a 705 variety of scenarios. The performance evaluation problem is two-fold. 706 One aspect of this problem relates to the evaluation of the system 707 level performance of the network. The other aspect relates to the 708 evaluation of the resource level performance, which restricts 709 attention to the performance analysis of individual network 710 resources. In this memo, we refer to the system level characteristics 711 of the network as the "macro-states" and the resource level 712 characteristics as the "micro-states." The system level 713 characteristics are also known as the emergent properties of the 714 network as noted earlier. Correspondingly, we shall refer to the 715 traffic engineering schemes dealing with network performance 716 optimization at the systems level as "macro-TE" and the schemes that 717 optimize at the individual resource level as "micro-TE." Under 718 certain circumstances, the system level performance can be derived 719 from the resource level performance using appropriate rules of 720 composition, depending upon the particular performance measures of 721 interest. 723 Another fundamental class of problems concerns how to effectively 724 optimize network performance. Performance optimization may entail 725 translating solutions to specific traffic engineering problems into 726 network configurations. Optimization may also entail some degree of 727 resource management control, routing control, and/or capacity 728 augmentation. 730 As noted previously, congestion is an undesirable phenomena in 731 operational networks. Therefore, the next subsection addresses the 732 issue of congestion and its ramifications within the problem context 733 of Internet traffic engineering. 735 2.3.1 Congestion and its Ramifications 737 Congestion is one of the most significant problems in an operational 738 IP context. A network element is said to be congested if it 739 experiences sustained overload over an interval of time. Congestion 740 almost always results in degradation of service quality to end users. 741 Congestion control schemes can include demand side policies and 742 supply side policies. Demand side policies may restrict access to 743 congested resources and/or dynamically regulate the demand to 744 alleviate the overload situation. Supply side policies may expand or 745 augment network capacity to better accommodate offered traffic. 746 Supply side policies may also re-allocate network resources by 747 redistributing traffic over the infrastructure. Traffic 748 redistribution and resource re-allocation serve to increase the 749 'effective capacity' seen by the demand. 751 The emphasis of this memo is primarily on congestion management 752 schemes falling within the scope of the network, rather than on 753 congestion management systems dependent upon sensitivity and 754 adaptivity from end-systems. That is, the aspects that are considered 755 in this memo with respect to congestion management are those 756 solutions that can be provided by control entities operating on the 757 network and by the actions of network administrators and network 758 operations systems. 760 2.4 Solution Context 762 The solution context for Internet traffic engineering involves 763 analysis, evaluation of alternatives, and choice between alternative 764 courses of action. Generally the solution context is predicated on 765 making reasonable inferences about the current or future state of the 766 network, and subsequently making appropriate decisions that may 767 involve a preference between alternative sets of action. More 768 specifically, the solution context demands reasonable estimates of 769 traffic workload, characterization of network state, deriving 770 solutions to traffic engineering problems which may be implicitly or 771 explicitly formulated, and possibly instantiating a set of control 772 actions. Control actions may involve the manipulation of parameters 773 associated with routing, control over tactical capacity acquisition, 774 and control over the traffic management functions. 776 The following list of instruments may be applicable to the solution 777 context of Internet traffic engineering. 779 (1) A set of policies, objectives, and requirements (which may be 780 context dependent) for network performance evaluation and 781 performance optimization. 783 (2) A collection of online and possibly offline tools and mechanisms 784 for measurement, characterization, modeling, and control 785 of Internet traffic and control over the placement and allocation 786 of network resources, as well as control over the mapping or 787 distribution of traffic onto the infrastructure. 789 (3) A set of constraints on the operating environment, the network 790 protocols, and the traffic engineering system itself. 792 (4) A set of quantitative and qualitative techniques and 793 methodologies for abstracting, formulating, and 794 solving traffic engineering problems. 796 (5) A set of administrative control parameters which may be 797 manipulated through a Configuration Management (CM) system. 798 The CM system itself may include a configuration control 799 subsystem, a configuration repository, a configuration 800 accounting subsystem, and a configuration auditing subsystem. 802 (6) A set of guidelines for network performance evaluation, 803 performance optimization, and performance improvement. 805 Derivation of traffic characteristics through measurement and/or 806 estimation is very useful within the realm of the solution space for 807 traffic engineering. Traffic estimates can be derived from customer 808 subscription information, traffic projections, traffic models, and 809 from actual empirical measurements. The empirical measurements may be 810 performed at the traffic aggregate level or at the flow level in 811 order to derive traffic statistics at various levels of detail. 812 Measurements at the flow level or on small traffic aggregates may be 813 performed at edge nodes, where traffic enters and leaves the network. 814 Measurements at large traffic aggregate levels may be performed 815 within the core of the network where potentially numerous traffic 816 flows may be in transit concurrently. 818 To conduct performance studies and to support planning of existing 819 and future networks, a routing analysis may be performed to determine 820 the path(s) the routing protocols will choose for various traffic 821 demands, and to ascertain the utilization of network resources as 822 traffic is routed through the network. The routing analysis should 823 capture the selection of paths through the network, the assignment of 824 traffic across multiple feasible routes, and the multiplexing of IP 825 traffic over traffic trunks (if such constructs exists) and over the 826 underlying network infrastructure. A network topology model is a 827 necessity for routing analysis. A network topology model may be 828 extracted from network architecture documents, from network designs, 829 from information contained in router configuration files, from 830 routing databases, from routing tables, or from automated tools that 831 discover and depict network topology information. Topology 832 information may also be derived from servers that monitor network 833 state, and from servers that perform provisioning functions. 835 Routing in operational IP networks can be administratively controlled 836 at various levels of abstraction including the manipulation of BGP 837 attributes and manipulation of IGP metrics. For path oriented 838 technologies such as MPLS, routing can be further controlled by the 839 manipulation of relevant traffic engineering parameters, resource 840 parameters, and administrative policy constraints. Within the 841 context of MPLS, the path of an explicit label switched path (LSP) 842 can be computed and established in various ways including: (1) 843 manually, (2) automatically online using constraint-based routing 844 processes implemented on label switching routers, and (3) 845 automatically offline using constraint-based routing entities 846 implemented on external traffic engineering support systems. 848 2.4.1 Combating the Congestion Problem 850 Minimizing congestion is a significant aspect of Internet traffic 851 engineering. This subsection gives an overview of the general 852 approaches that have been used or proposed to combat congestion 853 problems. 855 Congestion management policies can be categorized based upon the 856 following criteria (see e.g., [YARE95] for a more detailed taxonomy 857 of congestion control schemes): (1) Response time scale which can be 858 characterized as long, medium, or short; (2) reactive versus 859 preventive which relates to congestion control and congestion 860 avoidance; and (3) supply side versus demand side congestion 861 management schemes. These aspects are discussed in the following 862 paragraphs. 864 (1) Congestion Management based on Response Time Scales 866 - Long (weeks to months): Capacity planning works over a relatively 867 long time scale to expand network capacity based on estimates or 868 forecasts of future traffic demand and traffic distribution. Since 869 router and link provisioning take time and are generally expensive, 870 these upgrades are typically carried out in the weeks-to-months or 871 even years time scale. 873 - Medium (minutes to days): Several control policies fall within the 874 medium time scale category. Examples include: (1) Adjusting IGP 875 and/or BGP parameters to route traffic away or towards certain 876 segments of the network; (2) Setting up and/or adjusting some 877 explicitly routed label switched paths (ER-LSPs) in MPLS networks to 878 route some traffic trunks away from possibly congested resources or 879 towards possibly more favorable routes; (3) re-configuring the 880 logical topology of the network to make it correlate more closely 881 with the spatial traffic distribution using for example some 882 underlying path-oriented technology such as MPLS LSPs, ATM PVCs, or 883 optical channel trails. Many of these adaptive medium time scale 884 response schemes rely on a measurement system that monitors changes 885 in traffic distribution, traffic shifts, and network resource 886 utilization and subsequently provides feedback to the online and/or 887 offline traffic engineering mechanisms and tools which employ this 888 feedback information to trigger certain control actions to occur 889 within the network. The traffic engineering mechanisms and tools can 890 be implemented in a distributed fashion or in a centralized fashion, 891 and may have a hierarchical structure or a flat structure. The 892 comparative merits of distributed and centralized control structures 893 for networks are well known. A centralized scheme may have global 894 visibility into the network state and may produce potentially more 895 optimal solutions. However, centralized schemes are prone to single 896 points of failure and may not scale as well as distributed schemes. 897 Moreover, the information utilized by a centralized scheme may be 898 stale and may not reflect the actual state of the network. It is not 899 an objective of this memo to make a recommendation between 900 distributed and centralized schemes. This is a choice that network 901 administrators must make based on their specific needs. 903 - Short (picoseconds to minutes): This category includes packet level 904 processing functions and events on the order of several round trip 905 times. It includes router mechanisms such as passive and active 906 buffer management. These mechanisms are used to control congestion 907 and/or signal congestion to end systems so that they can adaptively 908 regulate the rate at which traffic is injected into the network. One 909 of the most popular active queue management schemes, especially for 910 TCP traffic, is Random Early Detection (RED) [FLJA93], which supports 911 congestion avoidance by controlling the average queue size. During 912 congestion (but before the queue is filled), the RED scheme chooses 913 arriving packets to "mark" according to a probabilistic algorithm 914 which takes into account the average queue size. For a router that 915 does not utilize explicit congestion notification (ECN) see e.g., 916 [FLOY94], the marked packets can simply be dropped to signal the 917 inception of congestion to end systems. On the other hand, if the 918 router supports ECN, then it can set the ECN field in the packet 919 header. Several variations of RED have been proposed to support 920 different drop precedence levels in multi-class environments [RFC- 921 2597], e.g., RED with In and Out (RIO) and Weighted RED. There is 922 general consensus that RED provides congestion avoidance performance 923 which is not worse than traditional Tail-Drop (TD) queue management 924 (drop arriving packets only when the queue is full). Importantly, 925 however, RED reduces the possibility of global synchronization and 926 improves fairness among different TCP sessions. However, RED by 927 itself can not prevent congestion and unfairness caused by sources 928 unresponsive to RED, e.g., UDP traffic and some misbehaved greedy 929 connections. Other schemes have been proposed to improve the 930 performance and fairness in the presence of unresponsive traffic. 931 Some of these schemes were proposed as theoretical frameworks and are 932 typically not available in existing commercial products. Two such 933 schemes are Longest Queue Drop (LQD) and Dynamic Soft Partitioning 934 with Random Drop (RND) [SLDC98]. 936 (2) Congestion Management: Reactive versus Preventive Schemes 938 - Reactive: reactive (recovery) congestion management policies react 939 to existing congestion problems to improve it. All the policies 940 described in the long and medium time scales above can be categorized 941 as being reactive especially if the policies are based on monitoring 942 and identifying existing congestion problems, and on the initiation 943 of relevant actions to ease a situation. 945 - Preventive: preventive (predictive/avoidance) policies take 946 proactive action to prevent congestion based on estimates and 947 predictions of future potential congestion problems. Some of the 948 policies described in the long and medium time scales fall into this 949 category. They do not necessarily respond immediately to existing 950 congestion problems. Instead forecasts of traffic demand and workload 951 distribution are considered and action may be taken to prevent 952 potential congestion problems in the future. The schemes described in 953 the short time scale (e.g., RED and its variations, ECN, LQD, and 954 RND) are also used for congestion avoidance since dropping or marking 955 packets before queues actually overflow would trigger corresponding 956 TCP sources to slow down. 958 (3) Congestion Management: Supply Side versus Demand Side Schemes 960 - Supply side: supply side congestion management policies increase 961 the effective capacity available to traffic in order to control or 962 obviate congestion. This can be accomplished by augmenting capacity. 963 Another way to accomplish this is to minimize congestion by having a 964 relatively balanced distribution of traffic over the network. For 965 example, capacity planning should aim to provide a physical topology 966 and associated link bandwidths that match estimated traffic workload 967 and traffic distribution based on forecasting (subject to budgetary 968 and other constraints). However, if actual traffic distribution does 969 not match the topology derived from capacity panning (due to 970 forecasting errors or facility constraints for example), then the 971 traffic can be mapped onto the existing topology using routing 972 control mechanisms, using path oriented technologies (e.g., MPLS LSPs 973 and optical channel trails) to modify the logical topology, or by 974 using some other load redistribution mechanisms. 976 - Demand side: demand side congestion management policies control or 977 regulate the offered traffic to alleviate congestion problems. For 978 example, some of the short time scale mechanisms described earlier 979 (such as RED and its variations, ECN, LQD, and RND) as well as 980 policing and rate shaping mechanisms attempt to regulate the offered 981 load in various ways. Tariffs may also be applied as a demand side 982 instrument. To date, however, tariffs have not been used as a means 983 of demand side congestion management within the Internet. 985 In summary, a variety of mechanisms can be used to address congestion 986 problems in IP networks. These mechanisms may operate at multiple 987 time-scales. 989 2.5 Implementation and Operational Context 991 The operational context of Internet traffic engineering is 992 characterized by constant change which occur at multiple levels of 993 abstraction. The implementation context demands effective planning, 994 organization, and execution. The planning aspects may involve 995 determining prior sets of actions to achieve desired objectives. 996 Organizing involves arranging and assigning responsibility to the 997 various components of the traffic engineering system and coordinating 998 the activities to accomplish the desired TE objectives. Execution 999 involves measuring and applying corrective or perfective actions to 1000 attain and maintain desired TE goals. 1002 3.0 Traffic Engineering Process Model(s) 1004 This section describes a generic process model that captures the high 1005 level practical aspects of Internet traffic engineering in an 1006 operational context. The process model is described as a sequence of 1007 actions that a traffic engineer, or more generally a traffic 1008 engineering system, must perform to optimize the performance of an 1009 operational network (see also [RFC-2702, AWD2]). The process model 1010 described here represents the broad activities common to most traffic 1011 engineering methodologies although the details regarding how traffic 1012 engineering is executed may differ from network to network. This 1013 process model may be enacted explicitly or implicitly, by an 1014 automaton and/or by a human. 1016 The traffic engineering process model is iterative [AWD2]. The four 1017 phases of the process model described below are repeated continually. 1019 The first phase of the TE process model is to define the relevant 1020 control policies that govern the operation of the network. These 1021 policies may depend upon many factors including the prevailing 1022 business model, the network cost structure, the operating 1023 constraints, the utility model, and optimization criteria. 1025 The second phase of the process model is a feedback mechanism 1026 involving the acquisition of measurement data from the operational 1027 network. If empirical data is not readily available from the network, 1028 then synthetic workloads may be used instead which reflect either the 1029 prevailing or the expected workload of the network. Synthetic 1030 workloads may be derived by estimation or extrapolation using prior 1031 empirical data. Their derivation may also be obtained using 1032 mathematical models of traffic characteristics or other means. 1034 The third phase of the process model is to analyze the network state 1035 and to characterize traffic workload. Performance analysis may be 1036 proactive and/or reactive. Proactive performance analysis identifies 1037 potential problems that do not exist, but could manifest in the 1038 future. Reactive performance analysis identifies existing problems, 1039 determines their cause through diagnosis, and evaluates alternative 1040 approaches to remedy the problem, if necessary. A number of 1041 quantitative and qualitative techniques may be used in the analysis 1042 process, including modeling based analysis and simulation. The 1043 analysis phase of the process model may involve investigating the 1044 concentration and distribution of traffic across the network or 1045 relevant subsets of the network, identifying the characteristics of 1046 the offered traffic workload, identifying existing or potential 1047 bottlenecks, and identifying network pathologies such as ineffective 1048 link placement, single points of failures, etc. Network pathologies 1049 may result from many factors including inferior network architecture, 1050 inferior network design, and configuration problems. A traffic 1051 matrix may be constructed as part of the analysis process. Network 1052 analysis may also be descriptive or prescriptive. 1054 The fourth phase of the TE process model is the performance 1055 optimization of the network. The performance optimization phase 1056 involves a decision process which selects and implements a set of 1057 actions from a set of alternatives. Optimization actions may include 1058 the use of appropriate techniques to either control the offered 1059 traffic or to control the distribution of traffic across the network. 1060 Optimization actions may also involve adding additional links or 1061 increasing link capacity, deploying additional hardware such as 1062 routers and switches, systematically adjusting parameters associated 1063 with routing such as IGP metrics and BGP attributes, and adjusting 1064 traffic management parameters. Network performance optimization may 1065 also involve starting a network planning process to improve the 1066 network architecture, network design, network capacity, network 1067 technology, and the configuration of network elements to accommodate 1068 current and future growth. 1070 3.1 Components of the Traffic Engineering Process Model 1072 The key components of the traffic engineering process model include a 1073 measurement subsystem, a modeling and analysis subsystem, and an 1074 optimization subsystem. The following subsections examine these 1075 components as they apply to the traffic engineering process model. 1077 3.2 Measurement 1079 Measurement is crucial to the traffic engineering function. The 1080 operational state of a network can be conclusively determined only 1081 through measurement. Measurement is also critical to the optimization 1082 function because it provides feedback data which is used by traffic 1083 engineering control subsystems. This data is used to adaptively 1084 optimize network performance in response to events and stimuli 1085 originating within and outside the network. Measurement is also 1086 needed to determine the quality of network services and to evaluate 1087 the effectiveness of traffic engineering policies. Experience 1088 suggests that measurement is most effective when acquired and applied 1089 systematically. 1091 When developing a measurement system to support the traffic 1092 engineering function in IP networks, the following questions should 1093 be carefully considered: Why is measurement needed in this particular 1094 context? What parameters are to be measured? How should the 1095 measurement be accomplished? Where should the measurement be 1096 performed? When should the measurement be performed? How frequently 1097 should the monitored variables be measured? What level of 1098 measurement accuracy and reliability is desirable? What level of 1099 measurement accuracy and reliability is realistically attainable? To 1100 what extent can the measurement system permissibly interfere with the 1101 monitored network components and variables? What is the acceptable 1102 cost of measurement? The answers to these questions will determine 1103 the measurement tools and methodologies appropriate in any given 1104 traffic engineering context. 1106 It should also be noted that there is a distinction between 1107 measurement and evaluation. Measurement provides raw data concerning 1108 state parameters and variables of monitored network elements. 1109 Evaluation utilizes the raw data to make inferences regarding the 1110 monitored system. 1112 Measurement in support of the TE function can occur at different 1113 levels of abstraction. For example, measurement can be used to derive 1114 packet level characteristics, flow level characteristics, user or 1115 customer level characteristics, traffic aggregate characteristics, 1116 component level characteristics, and network wide characteristics. 1118 3.3 Modeling, Analysis, and Simulation 1120 Modeling and analysis are important aspects of Internet traffic 1121 engineering. Modeling involves constructing an abstract or physical 1122 representation which depicts relevant traffic characteristics and 1123 network attributes. 1125 A network model is an abstract representation of the network which 1126 captures relevant network features, attributes, and characteristics, 1127 such as link and nodal attributes and constraints. A network model 1128 may facilitate analysis and/or simulation which can be used to 1129 predict network performance under various conditions as well as to 1130 guide network expansion plans. 1132 In general, Internet traffic engineering models can be classified as 1133 either structural or behavioral. Structural models focus on the 1134 organization of the network and its components. Behavioral models 1135 focus on the dynamics of the network and the traffic workload. 1136 Modeling for Internet traffic engineering may also be formal or 1137 informal. 1139 Accurate behavioral models for traffic sources are particularly 1140 useful for analysis. Development of behavioral traffic source models 1141 that are consistent with empirical data obtained from operational 1142 networks is a major research topic in Internet traffic engineering. 1143 These source models should also be tractable and amenable to 1144 analysis. The topic of source models for IP traffic is a research 1145 topic and is therefore outside the scope of this document. Its 1146 importance, however, must be emphasized. 1148 Network simulation tools are extremely useful for traffic 1149 engineering. Because of the complexity of realistic quantitative 1150 analysis of network behavior, certain aspects of network performance 1151 studies can only be conducted effectively using simulation. A good 1152 network simulator can be used to mimic and visualize network 1153 characteristics under various conditions in a safe and non-disruptive 1154 manner. For example, a network simulator may be used to depict 1155 congested resources and hot spots, and to provide hints regarding 1156 possible solutions to network performance problems. A good simulator 1157 may also be used to validate the effectiveness of planned solutions 1158 to network issues without the need to tamper with the operational 1159 network, or to commence an expensive network upgrade which may not 1160 achieve the desired objectives. Furthermore, during the process of 1161 network planning, a network simulator may reveal pathologies such as 1162 single points of failure which may require additional redundancy, and 1163 potential bottlenecks and hot spots which may require additional 1164 capacity. 1166 Routing simulators are especially useful in large networks. A routing 1167 simulator may identify planned links which may not actually be used 1168 to route traffic by the existing routing protocols. Simulators can 1169 also be used to conduct scenario based and perturbation based 1170 analysis, as well as sensitivity studies. Simulation results can be 1171 used to initiate appropriate actions in various ways. For example, an 1172 important application of network simulation tools is to investigate 1173 and identify how best to evolve and grow the network in order to 1174 accommodate projected future demands. 1176 3.4 Optimization 1178 Network performance optimization involves resolving network issues by 1179 transforming such issues into concepts that enable a solution, 1180 identification of a solution, and implementation of the solution. 1181 Network performance optimization can be corrective or perfective. In 1182 corrective optimization, the goal is to remedy a problem that has 1183 occurred or that is incipient. In perfective optimization, the goal 1184 is to improve network performance even when explicit problems do not 1185 exist and are not anticipated. 1187 Network performance optimization is a continual process, as noted 1188 previously. Performance optimization iterations may consist of 1189 real-time optimization sub-processes and non-real-time network 1190 planning sub-processes. The difference between real-time 1191 optimization and network planning is primarily in the relative time- 1192 scale in they operate and in the granularity of actions. One of the 1193 objectives of a real-time optimization sub-process is to control the 1194 mapping and distribution of traffic over the existing network 1195 infrastructure to avoid and/or relieve congestion, to assure 1196 satisfactory service delivery, and to optimize resource utilization. 1197 Real-time optimization is needed because random incidents such as 1198 fiber cuts or shifts in traffic demand will occur irrespective of how 1199 well a network is designed. These incidents can cause congestion and 1200 other problems to manifest in an operational network. Real-time 1201 optimization must solve such problems in small to medium time-scales 1202 ranging from micro-seconds to minutes or hours. Examples of real-time 1203 optimization include queue management, IGP/BGP metric tuning, and 1204 using technologies such as MPLS explicit LSPs to change the paths of 1205 some traffic trunks [XIAO]. 1207 One of the functions of the network planning sub-process is to 1208 initiate actions to systematically evolve the architecture, 1209 technology, topology, and capacity of a network. When a problem 1210 exists in the network, real-time optimization should provide an 1211 immediate remedy. Because a prompt response is necessary, the real- 1212 time solution may not be the best possible solution. Network 1213 planning may subsequently be needed to refine the solution and 1214 improve the situation. Network planning is also required to expand 1215 the network to support traffic growth and changes in traffic 1216 distribution over time. As previously noted, a change in the topology 1217 and/or capacity of the network may be the outcome of network 1218 planning. 1220 Clearly, network planning and real-time performance optimization are 1221 mutually complementary activities. A well-planned and designed 1222 network makes real-time optimization easier, while a systematic 1223 approach to real-time network performance optimization allows network 1224 planning to focus on long term issues rather than tactical 1225 considerations. Systematic real-time network performance 1226 optimization also provides valuable inputs and insights toward 1227 network planning. 1229 Stability is an important consideration in real-time network 1230 performance optimization. This aspect will be repeatedly addressed 1231 throughout this memo. 1233 4.0 Historical Review and Recent Developments 1235 This section briefly reviews different traffic engineering approaches 1236 proposed and implemented in telecommunications and computer networks. 1237 The discussion is not intended to be comprehensive. It is primarily 1238 intended to illuminate pre-existing perspectives and prior art 1239 concerning traffic engineering in the Internet and in legacy 1240 telecommunications networks. 1242 4.1 Traffic Engineering in Classical Telephone Networks 1244 This subsection presents a brief overview of traffic engineering in 1245 telephone networks which often relates to the way user traffic is 1246 steered from an originating node to the terminating node. This 1247 subsection presents a brief overview of this topic. A detailed 1248 description of the various routing strategies applied in telephone 1249 networks is included in the book by G. Ash [ASH2]. 1251 The early telephone network relied on static hierarchical routing, 1252 whereby routing patterns remained fixed independent of the state of 1253 the network or time of day. The hierarchy was intended to accommodate 1254 overflow traffic, improve network reliability via alternate routes, 1255 and prevent call looping by employing strict hierarchical rules. The 1256 network was typically over-provisioned since a given fixed route had 1257 to be dimensioned so that it could carry user traffic during a busy 1258 hour of any busy day. Hierarchical routing in the telephony network 1259 was found to be too rigid upon the advent of digital switches and 1260 stored program control which were able to manage more complicated 1261 traffic engineering rules. 1263 Dynamic routing was introduced to alleviate the routing inflexibility 1264 in the static hierarchical routing so that the network would operate 1265 more efficiently. This resulted in significant economic gains 1266 [HUSS87]. Dynamic routing typically reduces the overall loss 1267 probability by 10 to 20 percent (compared to static hierarchical 1268 routing). Dynamic routing can also improve network resilience by 1269 recalculating routes on a per-call basis and periodically updating 1270 routes. 1272 There are three main types of dynamic routing in the telephone 1273 network. They are time-dependent routing, state-dependent routing 1274 (SDR), and event dependent routing (EDR). 1276 In time-dependent routing, regular variations in traffic loads (such 1277 as time of day or day of week) are exploited in pre-planned routing 1278 tables. In state-dependent routing, routing tables are updated 1279 online according to the current state of the network (e.g, traffic 1280 demand, utilization, etc.). In event dependent routing, routing 1281 changes are incepted by events (such as call setups encountering 1282 congested or blocked links) whereupon new paths are searched out 1283 using learning models. EDR methods are real-time adaptive, but they 1284 do not require global state information as does SDR. Examples of EDR 1285 schemes include the dynamic alternate routing (DAR) from BT, the 1286 state-and-time dependent routing (STR) from NTT, and the success-to- 1287 the-top (STT) routing from AT&T. 1289 Dynamic non-hierarchical routing (DNHR) is an example of dynamic 1290 routing that was introduced in the AT&T toll network in the 1980's to 1291 respond to time-dependent information such as regular load variations 1292 as a function of time. Time-dependent information in terms of load 1293 may be divided into three time scales: hourly, weekly, and yearly. 1294 Correspondingly, three algorithms are defined to pre-plan the routing 1295 tables. The network design algorithm operates over a year-long 1296 interval while the demand servicing algorithm operates on a weekly 1297 basis to fine tune link sizes and routing tables to correct forecast 1298 errors on the yearly basis. At the smallest time scale, the routing 1299 algorithm is used to make limited adjustments based on daily traffic 1300 variations. Network design and demand servicing are computed using 1301 offline calculations. Typically, the calculations require extensive 1302 search on possible routes. On the other hand, routing may need 1303 online calculations to handle crankback. DNHR adopts a "two-link" 1304 approach whereby a path can consist of two links at most. The 1305 routing algorithm presents an ordered list of route choices between 1306 an originating switch and a terminating switch. If a call overflows, 1307 a via switch (a tandem exchange between the originating switch and 1308 the terminating switch) would send a crankback signal to the 1309 originating switch. This switch would then select the next route, 1310 and so on, until there are no alternative routes available in which 1311 the call is blocked. 1313 4.2 Evolution of Traffic Engineering in Packet Networks 1315 This subsection reviews related prior work that was intended to 1316 improve the performance of data networks. Indeed, optimization of 1317 the performance of data networks started in the early days of the 1318 ARPANET. Other early commercial networks such as SNA also recognized 1319 the importance of performance optimization and service 1320 differentiation. 1322 In terms of traffic management, the Internet has been a best effort 1323 service environment until recently. In particular, very limited 1324 traffic management capabilities existed in IP networks to provide 1325 differentiated queue management and scheduling services to packets 1326 belonging to different classes. 1328 In terms of routing control, the Internet has employed distributed 1329 protocols for intra-domain routing. These protocols are highly 1330 scalable and resilient. However, they are based on simple algorithms 1331 for path selection which have very limited functionality to allow 1332 flexible control of the path selection process. 1334 In the following subsections, the evolution of practical traffic 1335 engineering mechanisms in IP networks and its predecessors is 1336 reviewed. 1338 4.2.1 Adaptive Routing in the ARPANET 1340 The early ARPANET recognized the importance of adaptive routing where 1341 routing decisions were based on the current state of the network 1342 [MCQ80]. Early minimum delay routing approaches forwarded each 1343 packet to its destination along a path for which the total estimated 1344 transit time was the smallest. Each node maintained a table of 1345 network delays, representing the estimated delay that a packet would 1346 experience along a given path toward its destination. The minimum 1347 delay table was periodically transmitted by a node to its neighbors. 1348 The shortest path, in terms of hop count, was also propagated to give 1349 the connectivity information. 1351 One drawback to this approach is that dynamic link metrics tend to 1352 create "traffic magnets" causing congestion to be shifted from one 1353 location of a network to another location, resulting in oscillation 1354 and network instability. 1356 4.2.2 Dynamic Routing in the Internet 1358 The Internet evolved from the APARNET and adopted dynamic routing 1359 algorithms with distributed control to determine the paths that 1360 packets should take en-route to their destinations. The routing 1361 algorithms are adaptations of shortest path algorithms where costs 1362 are based on link metrics. The link metric can be based on static or 1363 dynamic quantities. The link metric based on static quantities may be 1364 assigned administratively according to local criteria. The link 1365 metric based on dynamic quantities may be a function of a network 1366 congestion measure such as delay or packet loss. 1368 It was apparent early that static link metric assignment was 1369 inadequate because it can easily lead to unfavorable scenarios in 1370 which some links become congested while others remain lightly loaded. 1371 One of the many reasons for the inadequacy of static link metrics is 1372 that link metric assignment was often done without considering the 1373 traffic matrix in the network. Also, the routing protocols did not 1374 take traffic attributes and capacity constraints into account when 1375 making routing decisions. This results in traffic concentration being 1376 localized in subsets of the network infrastructure and potentially 1377 causing congestion. Even if link metrics are assigned in accordance 1378 with the traffic matrix, unbalanced loads in the network can still 1379 occur due to a number factors including: 1381 - Resources may not be deployed in the most optimal locations 1382 from a routing perspective. 1384 - Forecasting errors in traffic volume and/or traffic distribution. 1386 - Dynamics in traffic matrix due to the temporal nature of traffic 1387 patterns, BGP policy change from peers, etc. 1389 The inadequacy of the legacy Internet interior gateway routing system 1390 is one of the factors motivating the interest in path oriented 1391 technology with explicit routing and constraint-based routing 1392 capability such as MPLS. 1394 4.2.3 ToS Routing 1396 Type-of-Service (ToS) routing involves different routes going to the 1397 same destination being selected depending upon the ToS field of an IP 1398 packet [RFC-1349]. The ToS classes may be classified as low delay 1399 and high throughput. Each link is associated with multiple link 1400 costs and each link cost is used to compute routes for a particular 1401 ToS. A separate shortest path tree is computed for each ToS. The 1402 shortest path algorithm must be run for each ToS resulting in very 1403 expensive computation. Classical ToS-based routing is now outdated 1404 as the IP header field has been replaced by a Diffserv field. 1405 Effective traffic engineering is difficult to perform in classical 1406 ToS-based routing because each class still relies exclusively on 1407 shortest path routing which results in localization of traffic 1408 concentration within the network. 1410 4.2.4 Equal Cost Multi-Path 1412 Equal Cost Multi-Path (ECMP) is another technique that attempts to 1413 address the deficiency in Shortest Path First (SPF) interior gateway 1414 routing systems [RFC-2178]. In the classical SPF algorithm, if two or 1415 more shortest paths exist to a given destination, the algorithm will 1416 choose one of them. The algorithm is modified slightly in ECMP so 1417 that if two or more equal cost shortest paths exist between two 1418 nodes, the traffic between the nodes is distributed among the 1419 multiple equal-cost paths. Traffic distribution across the equal- 1420 cost paths is usually performed in one of two ways: (1) packet-based 1421 in a round-robin fashion, or (2) flow-based using hashing on source 1422 and destination IP addresses and possibly other fields of the IP 1423 header. The first approach can easily cause out-of-order packets 1424 while the second approach is dependent upon the number and 1425 distribution of flows. Flow-based load sharing may be unpredictable 1426 in an enterprise network where the number of flows is relatively 1427 small and less heterogeneous (for example, hashing may not be 1428 uniform), but it is generally effective in core public networks where 1429 the number of flows is large and heterogeneous. 1431 In ECMP, link costs are static and bandwidth constraints are not 1432 considered, so ECMP attempts to distribute the traffic as equally as 1433 possible among the equal-cost paths independent of the congestion 1434 status of each path. As a result, given two equal-cost paths, it is 1435 possible that one of the paths will be more congested than the other. 1436 Another drawback of ECMP is that load sharing cannot be achieved on 1437 multiple paths which have non-identical costs. 1439 4.2.5 Nimrod 1441 Nimrod is a routing system developed to provide heterogeneous service 1442 specific routing in the Internet, while taking multiple constraints 1443 into account [RFC-1992]. Essentially, Nimrod is a link state routing 1444 protocol which supports path oriented packet forwarding. It uses the 1445 concept of maps to represent network connectivity and services at 1446 multiple levels of abstraction. Mechanisms are provided to allow 1447 restriction of the distribution of routing information. 1449 Even though Nimrod did not enjoy deployment in the public Internet, a 1450 number of key concepts incorporated into the Nimrod architecture, 1451 such as explicit routing which allows selection of paths at 1452 originating nodes, are beginning to find applications in some recent 1453 constraint-based routing initiatives. 1455 4.3 Overlay Model 1457 In the overlay model, a virtual-circuit network, such as ATM, frame 1458 relay, or WDM provides virtual-circuit connectivity between routers 1459 that are located at the edges of a virtual-circuit cloud. In this 1460 mode, two routers that are connected through a virtual circuit see a 1461 direct adjacency between themselves independent of the physical route 1462 taken by the virtual circuit through the ATM, frame relay, or WDM 1463 network. Thus, the overlay model essentially decouples the logical 1464 topology that routers see from the physical topology that the ATM, 1465 frame relay, or WDM network manages. The overlay model based on ATM 1466 or frame relay enables a network administrator or an automaton to 1467 employ traffic engineering concepts to perform path optimization by 1468 re-configuring or rearranging the virtual circuits so that a virtual 1469 circuit on a congested or sub-optimal physical link can be re-routed 1470 to a less congested or more optimal one. In the overlay model, 1471 traffic engineering is also employed to establish relationships 1472 between the traffic management parameters (e.g. PCR, SCR, and MBS for 1473 ATM) of the virtual-circuit technology and the actual traffic that 1474 traverses each circuit. These relationships can be established based 1475 upon known or projected traffic profiles, and some other factors. 1477 The overlay model using IP over ATM requires the management of two 1478 separate networks with different technologies (IP and ATM) resulting 1479 in increased operational complexity and cost. In the fully-meshed 1480 overlay model, each router would peer to every other router in the 1481 network, so that the total number of adjacencies is a quadratic 1482 function of the number of routers. Some of the issues with the 1483 overlay model are discussed in [AWD2]. 1485 4.4 Constrained-Based Routing 1487 Constraint-based routing refers to a class of routing systems that 1488 compute routes through a network subject to satisfaction of a set of 1489 constraints and requirements. In the most general setting, 1490 constraint-based routing may also seek to optimize overall network 1491 performance while minimizing costs. 1493 The constraints and requirements may be imposed by the network itself 1494 or by administrative policies. Constraints may include bandwidth, hop 1495 count, delay, and policy instruments such as resource class 1496 attributes. Constraints may also include domain specific attributes 1497 of certain network technologies and contexts which impose 1498 restrictions on the solution space of the routing function. Path 1499 oriented technologies such as MPLS have made constraint-based routing 1500 feasible and attractive in public IP networks. 1502 The concept of constraint-based routing within the context of MPLS 1503 traffic engineering requirements in IP networks was first defined in 1504 [RFC-2702]. 1506 Unlike QoS routing (for example, see [RFC-2386] and [MA]) which 1507 generally addresses the issue of routing individual traffic flows to 1508 satisfy prescribed flow based QoS requirements subject to network 1509 resource availability, constraint-based routing is applicable to 1510 traffic aggregates as well as flows and may be subject to a wide 1511 variety of constraints which may include policy restrictions. 1513 4.5 Overview of Other IETF Projects Related to Traffic Engineering 1515 This subsection reviews a number of IETF activities pertinent to 1516 Internet traffic engineering. These activities are primarily intended 1517 to evolve the IP architecture to support new service definitions 1518 which allow preferential or differentiated treatment to be accorded 1519 to certain types of traffic. 1521 4.5.1 Integrated Services 1523 The IETF Integrated Services working group developed the integrated 1524 services (Intserv) model. This model requires resources, such as 1525 bandwidth and buffers, to be reserved a priori for a given traffic 1526 flow to ensure that the quality of service requested by the traffic 1527 flow is satisfied. The integrated services model includes additional 1528 components beyond those used in the best-effort model such as packet 1529 classifiers, packet schedulers, and admission control. A packet 1530 classifier is used to identify flows that are to receive a certain 1531 level of service. A packet scheduler handles the scheduling of 1532 service to different packet flows to ensure that QoS commitments are 1533 met. Admission control is used to determine whether a router has the 1534 necessary resources to accept a new flow. 1536 Two services have been defined under the Integrated Services model: 1537 guaranteed service [RFC-2212] and controlled-load service [RFC-2211]. 1539 The guaranteed service can be used for applications requiring bounded 1540 packet delivery time. For this type of application, data that is 1541 delivered to the application after a pre-defined amount of time has 1542 elapsed is usually considered worthless. Therefore, guaranteed 1543 service was intended to provide a firm quantitative bound on the 1544 end-to-end packet delay for a flow. This is accomplished by 1545 controlling the queuing delay on network elements along the data flow 1546 path. The guaranteed service model does not, however, provide bounds 1547 on jitter (inter-arrival times between consecutive packets). 1549 The controlled-load service can be used for adaptive applications 1550 that can tolerate some delay but are sensitive to traffic overload 1551 conditions. This type of application typically functions 1552 satisfactorily when the network is lightly loaded but its performance 1553 degrades significantly when the network is heavily loaded. 1554 Controlled-load service therefore has been designed to provide 1555 approximately the same service as best-effort service in a lightly 1556 loaded network regardless of actual network conditions. Controlled- 1557 load service is described qualitatively in that no target values of 1558 delay or loss are specified. 1560 The main issue with the Integrated Services model has been 1561 scalability [RFC-2998], especially in large public IP networks which 1562 may potentially have millions of active micro-flows in transit 1563 concurrently. 1565 A notable feature of the Integrated Services model is that it 1566 requires explicit signaling of QoS requirements from end systems to 1567 routers [RFC-2753]. The Resource Reservation Protocol (RSVP) performs 1568 this signaling function and is a critical component of the Integrated 1569 Services model. The RSVP protocol is described next. 1571 4.5.2 RSVP 1573 RSVP is a soft state signaling protocol [RFC-2205]. It supports 1574 receiver initiated establishment of resource reservations for both 1575 multicast and unicast flows. RSVP was originally developed as a 1576 signaling protocol within the integrated services framework for 1577 applications to communicate QoS requirements to the network and for 1578 the network to reserve relevant resources to satisfy the QoS 1579 requirements [RFC-2205]. 1581 Under RSVP, the sender or source node sends a PATH message to the 1582 receiver with the same source and destination addresses as the 1583 traffic which the sender will generate. The PATH message contains: 1584 (1) a sender Tspec specifying the characteristics of the traffic, (2) 1585 a sender Template specifying the format of the traffic, and (3) an 1586 optional Adspec which is used to support the concept of one pass with 1587 advertising" (OPWA) [RFC-2205]. Every intermediate router along the 1588 path forwards the PATH Message to the next hop determined by the 1589 routing protocol. Upon receiving a PATH Message, the receiver 1590 responds with a RESV message which includes a flow descriptor used to 1591 request resource reservations. The RESV message travels to the sender 1592 or source node in the opposite direction along the path that the PATH 1593 message traversed. Every intermediate router along the path can 1594 reject or accept the reservation request of the RESV message. If the 1595 request is rejected, the rejecting router will send an error message 1596 to the receiver and the signaling process will terminate. If the 1597 request is accepted, link bandwidth and buffer space are allocated 1598 for the flow and the related flow state information is installed in 1599 the router. 1601 One of the issues with the original RSVP specification was 1602 Scalability. This is because reservations were required for micro- 1603 flows, so that the amount of state maintained by network elements 1604 tends to increase linearly with the number of micro-flows. These 1605 issues are described in [RFC-2961]. 1607 Recently, RSVP has been modified and extended in several ways to 1608 mitigate the scaling problems. As a result, it is becoming a 1609 versatile signaling protocol for the Internet. For example, RSVP has 1610 been extended to reserve resources for aggregation of flows, to set 1611 up MPLS explicit label switched paths, and to perform other signaling 1612 functions within the Internet. There are also a number of proposals 1613 to reduce the amount of refresh messages required to maintain 1614 established RSVP sessions [RFC-2961]. 1616 A number of IETF working groups have been engaged in activities 1617 related to the RSVP protocol. These include the original RSVP working 1618 group, the MPLS working group, the Resource Allocation Protocol 1619 working group, and the Policy Framework working group. 1621 4.5.3 Differentiated Services 1623 The goal of the Differentiated Services (Diffserv) effort within the 1624 IETF is to devise scalable mechanisms for categorization of traffic 1625 into behavior aggregates, which ultimately allows each behavior 1626 aggregate to be treated differently, especially when there is a 1627 shortage of resources such as link bandwidth and buffer space [RFC- 1628 2475]. One of the primary motivations for the Diffserv effort was to 1629 devise alternative mechanisms for service differentiation in the 1630 Internet that mitigate the scalability issues encountered with the 1631 Intserv model. 1633 The IETF Diffserv working group has defined a Differentiated Services 1634 field in the IP header (DS field). The DS field consists of six bits 1635 of the part of the IP header formerly known as TOS octet. The DS 1636 field is used to indicate the forwarding treatment that a packet 1637 should receive at a node [RFC-2474]. The Diffserv working group has 1638 also standardized a number of Per-Hop Behavior (PHB) groups. Using 1639 the PHBs, several classes of services can be defined using different 1640 classification, policing, shaping and scheduling rules. 1642 For an end-user of network services to receive Differentiated 1643 Services from its Internet Service Provider (ISP), it may be 1644 necessary for the user to have a Service Level Agreement (SLA) with 1645 the ISP. An SLA may explicitly or implicitly specify a Traffic 1646 Conditioning Agreement (TCA) which defines classifier rules as well 1647 as metering, marking, discarding, and shaping rules. 1649 Packets are classified, and possibly policed and shaped at the 1650 ingress to a Diffserv network. When a packet traverses the boundary 1651 between different Diffserv domains, the DS field of the packet may be 1652 re-marked according to existing agreements between the domains. 1654 Differentiated Services allows only a finite number of service 1655 classes to be indicated by the DS field. The main advantage of the 1656 Diffserv approach relative to the Intserv model is scalability. 1657 Resources are allocated on a per-class basis and the amount of state 1658 information is proportional to the number of classes rather than to 1659 the number of application flows. 1661 It should be obvious from the previous discussion that the Diffserv 1662 model essentially deals with traffic management issues on a per hop 1663 basis. The Diffserv control model consists of a collection of micro- 1664 TE control mechanisms. Other traffic engineering capabilities, such 1665 as capacity management (including routing control), are also required 1666 in order to deliver acceptable service quality in Diffserv networks. 1668 4.5.4 MPLS 1670 MPLS is an advanced forwarding scheme which also includes extensions 1671 to conventional IP control plane protocols. MPLS extends the Internet 1672 routing model and enhances packet forwarding and path control [RFC- 1673 3031]. 1675 At the ingress to an MPLS domain, label switching routers (LSRs) 1676 classify IP packets into forwarding equivalence classes (FECs) based 1677 on a variety of factors, including e.g. a combination of the 1678 information carried in the IP header of the packets and the local 1679 routing information maintained by the LSRs. An MPLS label is then 1680 prepended to each packet according to their forwarding equivalence 1681 classes. In a non-ATM/FR environment, the label is 32 bits long and 1682 contains a 20-bit label field, a 3-bit experimental field (formerly 1683 known as Class-of-Service or CoS field), a 1-bit label stack 1684 indicator and an 8-bit TTL field. In an ATM (FR) environment, the 1685 label consists information encoded in the VCI/VPI (DLCI) field. An 1686 MPLS capable router (an LSR) examines the label and possibly the 1687 experimental field and uses this information to make packet 1688 forwarding decisions. 1690 An LSR makes forwarding decisions by using the label prepended to 1691 packets as the index into a local next hop label forwarding entry 1692 (NHLFE). The packet is then processed as specified in the NHLFE. The 1693 incoming label may be replaced by an outgoing label, and the packet 1694 may be switched to the next LSR. This label-switching process is very 1695 similar to the label (VCI/VPI) swapping process in ATM networks. 1696 Before a packet leaves an MPLS domain, its MPLS label may be removed. 1697 A Label Switched Path (LSP) is the path between an ingress LSRs and 1698 an egress LSRs through which a labeled packet traverses. The path of 1699 an explicit LSP is defined at the originating (ingress) node of the 1700 LSP. MPLS can use a signaling protocol such as RSVP or LDP to set up 1701 LSPs. 1703 MPLS is a very powerful technology for Internet traffic engineering 1704 because it supports explicit LSPs which allow constraint-based 1705 routing to be implemented efficiently in IP networks [AWD2]. The 1706 requirements for traffic engineering over MPLS are described in 1707 [RFC-2702]. Extensions to RSVP to support instantiation of explicit 1708 LSP are discussed in [AWD3]. Extensions to LDP, known as CR-LDP, to 1709 support explicit LSPs are presented in [JAM]. 1711 4.5.5 IP Performance Metrics 1713 The IETF IP Performance Metrics (IPPM) working group has been 1714 developing a set of standard metrics that can be used to monitor the 1715 quality, performance, and reliability of Internet services. These 1716 metrics can be applied by network operators, end-users, and 1717 independent testing groups to provide users and service providers 1718 with a common understanding of the performance and reliability of the 1719 Internet component 'clouds' they use/provide [RFC2330]. The criteria 1720 for performance metrics developed by the IPPM WG are described in 1721 [RFC2330]. Examples of performance metrics include one-way packet 1722 loss [RFC2680], one-way delay [RFC2679], and connectivity measures 1723 between two nodes [RFC2678]. Other metrics include second-order 1724 measures of packet loss and delay. 1726 Some of the performance metrics specified by the IPPM WG are useful 1727 for specifying Service Level Agreements (SLAs). SLAs are sets of 1728 service level objectives negotiated between users and service 1729 providers, wherein each objective is a combination of one or more 1730 performance metrics possibly subject to certain constraints. 1732 4.5.6 Flow Measurement 1734 The IETF Real Time Flow Measurement (RTFM) working group has produced 1735 an architecture document defining a method to specify traffic flows 1736 as well as a number of components for flow measurement (meters, meter 1737 readers, manager) [RFC-2722]. A flow measurement system enables 1738 network traffic flows to be measured and analyzed at the flow level 1739 for a variety of purposes. As noted in RFC-2722, a flow measurement 1740 system can be very useful in the following contexts: (1) 1741 understanding the behavior of existing networks, (2) planning for 1742 network development and expansion, (3) quantification of network 1743 performance, (4) verifying the quality of network service, and (5) 1744 attribution of network usage to users. 1746 A flow measurement system consists of meters, meter readers, and 1747 managers. A meter observe packets passing through a measurement 1748 point, classifies them into certain groups, accumulates certain usage 1749 data (such as the number of packets and bytes for each group), and 1750 stores the usage data in a flow table. A group may represent a user 1751 application, a host, a network, a group of networks, etc. A meter 1752 reader gathers usage data from various meters so it can be made 1753 available for analysis. A manager is responsible for configuring and 1754 controlling meters and meter readers. The instructions received by a 1755 meter from a manager include flow specification, meter control 1756 parameters, and sampling techniques. The instructions received by a 1757 meter reader from a manager include the address of the meter whose 1758 date is to be collected, the frequency of data collection, and the 1759 types of flows to be collected. 1761 4.5.7 Endpoint Congestion Management 1763 The IETF Endpoint Congestion Management working group is intended to 1764 provide a set of congestion control mechanisms that transport 1765 protocols can use. It is also intended to develop mechanisms for 1766 unifying congestion control across a subset of an endpoint's active 1767 unicast connections (called a congestion group). A congestion 1768 manager continuously monitors the state of the path for each 1769 congestion group under its control. The manager uses that 1770 information to instruct a scheduler on how to partition bandwidth 1771 among the connections of that congestion group. 1773 4.6 Overview of ITU Activities Related to Traffic Engineering 1775 This section provides an overview of prior work within the ITU-T 1776 pertaining to traffic engineering in traditional telecommunications 1777 networks. 1779 ITU-T Recommendations E.600 [ITU-E600], E.701 [ITU-E701], and E.801 1780 [ITU-E801] address traffic engineering issues in traditional 1781 telecommunications networks. Recommendation E.600 provides a 1782 vocabulary for describing traffic engineering concepts, while E.701 1783 defines reference connections, Grade of Service (GOS), and traffic 1784 parameters for ISDN. Recommendation E.701 uses the concept of a 1785 reference connection to identify representative cases of different 1786 types of connections without describing the specifics of their actual 1787 realizations by different physical means. As defined in 1788 Recommendation E.600, "a connection is an association of resources 1789 providing means for communication between two or more devices in, or 1790 attached to, a telecommunication network." Also, E.600 defines "a 1791 resource as any set of physically or conceptually identifiable 1792 entities within a telecommunication network, the use of which can be 1793 unambiguously determined" [ITU-E600]. There can be different types 1794 of connections as the number and types of resources in a connection 1795 may vary. 1797 Typically, different network segments are involved in the path of a 1798 connection. For example, a connection may be local, national, or 1799 international. The purposes of reference connections are to clarify 1800 and specify traffic performance issues at various interfaces between 1801 different network domains. Each domain may consist of one or more 1802 service provider networks. 1804 Reference connections provide a basis to define grade of service 1805 (GoS) parameters related to traffic engineering within the ITU-T 1806 framework. As defined in E.600, "GoS refers to a number of traffic 1807 engineering variables which are used to provide a measure of the 1808 adequacy of a group of resources under specified conditions." These 1809 GoS variables may be probability of loss, dial tone, delay, etc. 1810 They are essential for network internal design and operation as well 1811 as for component performance specification. 1813 GoS is different from quality of service (QoS) in the ITU framework. 1814 QoS is the performance perceivable by a telecommunication service 1815 user and expresses the user's degree of satisfaction of the service. 1816 QoS parameters focus on performance aspects observable at the service 1817 access points and network interfaces, rather than their causes within 1818 the network. GoS, on the other hand, is a set of network oriented 1819 measures which characterize the adequacy of a group of resources 1820 under specified conditions. For a network to be effective in serving 1821 its users, the values of both GoS and QoS parameters must be related, 1822 with GoS parameters typically making a major contribution to the QoS. 1824 Recommendation E.600 stipulates that a set of GoS parameters must be 1825 selected and defined on an end-to-end basis for each major service 1826 category provided by a network to assist the network provider improve 1827 efficiency and effectiveness of the network. Based on a selected set 1828 of reference connections, suitable target values are assigned to the 1829 selected GoS parameters under normal and high load conditions. These 1830 end-to-end GoS target values are then apportioned to individual 1831 resource components of the reference connections for dimensioning 1832 purposes. 1834 4.7 Content Distribution 1836 The Internet is dominated by client-server interactions, especially 1837 Web traffic (in the future, more sophisticated media servers may 1838 become dominant). The location and performance of major information 1839 servers has a significant impact on the traffic patterns within the 1840 Internet as well as on the perception of service quality by end 1841 users. 1843 A number of dynamic load balancing techniques have been devised to 1844 improve the performance of replicated information servers. These 1845 techniques can cause spatial traffic characteristics to become more 1846 dynamic in the Internet because information servers can be 1847 dynamically picked based upon the location of the clients, the 1848 location of the servers, the relative utilization of the servers, the 1849 relative performance of different networks, and the relative 1850 performance of different parts of a network. This process of 1851 assignment of distributed servers to clients is called Traffic 1852 Directing. It functions at the application layer. 1854 Traffic Directing schemes that allocate servers in multiple 1855 geographically dispersed locations to clients may require empirical 1856 network performance statistics to make more effective decisions. In 1857 the future, network measurement systems may need to provide this type 1858 of information. The exact parameters needed are not yet defined. 1859 When congestion exists in the network, Traffic Directing and Traffic 1860 Engineering systems should act in a coordinated manner. This topic is 1861 for further study. 1863 The issues related to location and replication of information 1864 servers, particularly web servers, are important for Internet traffic 1865 engineering because these servers contribute a substantial proportion 1866 of Internet traffic. 1868 5.0 Taxonomy of Traffic Engineering Systems 1870 This section presents a short taxonomy of traffic engineering 1871 systems. A taxonomy of traffic engineering systems can be constructed 1872 based on traffic engineering styles and views as listed below: 1874 - Time-dependent vs State-dependent vs Event-dependent 1875 - Offline vs Online 1876 - Centralized vs Distributed 1877 - Local vs Global Information 1878 - Prescriptive vs Descriptive 1879 - Open Loop vs Closed Loop 1880 - Tactical vs Strategic 1882 These classification systems are described in greater detail in the 1883 following subsections of this document. 1885 5.1 Time-Dependent Versus State-Dependent Versus Event Dependent 1887 Traffic engineering methodologies can be classified as time-dependent 1888 or state-dependent or event-dependent. All TE schemes are considered 1889 to be dynamic in this framework. Static TE implies that no traffic 1890 engineering methodology or algorithm is being applied. 1892 In the time-dependent TE, historical information based on periodic 1893 variations in traffic (such as time of day) is used to pre-program 1894 routing plans and other TE control mechanisms. Additionally, 1895 customer subscription or traffic projection may be used. Pre- 1896 programmed routing plans typically change on a relatively long time 1897 scale (e.g., diurnal). Time-dependent algorithms do not attempt to 1898 adapt to random variations in traffic or changing network conditions. 1899 An example of a time-dependent algorithm is a global centralized 1900 optimizer where the input to the system is a traffic matrix and 1901 multi-class QoS requirements as described [MR99]. 1903 State-dependent TE adapts the routing plans for packets based on the 1904 current state of the network. The current state of the network 1905 provides additional information on variations in actual traffic 1906 (i.e., perturbations from regular variations) that could not be 1907 predicted using historical information. Constraint-based routing is 1908 an example of state-dependent TE operating in a relatively long time 1909 scale. An example operating in a relatively short time scale is a 1910 load-balancing algorithm described in [MATE]. 1912 The state of the network can be based on parameters such as 1913 utilization, packet delay, packet loss, etc. These parameters can be 1914 obtained in several ways. For example, each router may flood these 1915 parameters periodically or by means of some kind of trigger to other 1916 routers. Another approach is for a particular router performing 1917 adaptive TE to send probe packets along a path to gather the state of 1918 that path. Still another approach is for a management system to 1919 gather relevant information from network elements. 1921 Expeditious and accurate gathering and distribution of state 1922 information is critical for adaptive TE due to the dynamic nature of 1923 network conditions. State-dependent algorithms may be applied to 1924 increase network efficiency and resilience. Time-dependent algorithms 1925 are more suitable for predictable traffic variations. On the other 1926 hand, state-dependent algorithms are more suitable for adapting to 1927 the prevailing network state. 1929 Event-dependent TE methods can also be used for TE path selection. 1930 Event-dependent TE methods are distinct from time-dependent and 1931 state-dependent TE methods in the manner in which paths are selected. 1932 These algorithms are adaptive and distributed in nature and typically 1933 use learning models to find good paths for TE in a network. While 1934 state-dependent TE models typically use available-link-bandwidth 1935 (ALB) flooding for TE path selection, event-dependent TE methods do 1936 not require ALB flooding. Rather, event-dependent TE methods 1937 typically search out capacity by learning models, as in the success- 1938 to-the-top (STT) method. ALB flooding can be resource intensive, 1939 since it requires link bandwidth to carry LSAs, processor capacity to 1940 process LSAs, and the overhead can limit area/autonomous system (AS) 1941 size. Modeling results suggest that event-dependent TE methods could 1942 lead to a reduction in ALB flooding overhead without loss of network 1943 throughput performance [ASH3]. 1945 5.2 Offline Versus Online 1947 Traffic engineering requires the computation of routing plans. The 1948 computation may be performed offline or online. The computation can 1949 be done offline for scenarios where routing plans need not be 1950 executed in real-time. For example, routing plans computed from 1951 forecast information may be computed offline. Typically, offline 1952 computation is also used to perform extensive searches on multi- 1953 dimensional solution spaces. 1955 Online computation is required when the routing plans must adapt to 1956 changing network conditions as in state-dependent algorithms. Unlike 1957 offline computation (which can be computationally demanding), online 1958 computation is geared toward relative simple and fast calculations to 1959 select routes, fine-tune the allocations of resources, and perform 1960 load balancing. 1962 5.3 Centralized Versus Distributed 1964 Centralized control has a central authority which determines routing 1965 plans and perhaps other TE control parameters on behalf of each 1966 router. The central authority collects the network-state information 1967 from all routers periodically and returns the routing information to 1968 the routers. The routing update cycle is a critical parameter 1969 directly impacting the performance of the network being controlled. 1970 Centralized control may need high processing power and high bandwidth 1971 control channels. 1973 Distributed control determines route selection by each router 1974 autonomously based on the routers view of the state of the network. 1975 The network state information may be obtained by the router using a 1976 probing method or distributed by other routers on a periodic basis 1977 using link state advertisements. Network state information may also 1978 be disseminated under exceptional conditions. 1980 5.4 Local Versus Global 1982 Traffic engineering algorithms may require local or global network- 1983 state information. 1985 Local information pertains to the state of a portion of the domain. 1986 Examples include the bandwidth and packet loss rate of a particular 1987 path. Local state information may be sufficient for certain 1988 instances of distributed-controlled TEs. 1990 Global information pertains to the state of the entire domain 1991 undergoing traffic engineering. Examples include a global traffic 1992 matrix and loading information on each link throughout the domain of 1993 interest. Global state information is typically required with 1994 centralized control. Distributed TE systems may also need global 1995 information in some cases. 1997 5.5 Prescriptive Versus Descriptive 1999 TE systems may also be classified as prescriptive or descriptive. 2001 Prescriptive traffic engineering evaluates alternatives and 2002 recommends a course of action. Prescriptive traffic engineering can 2003 be further categorized as either corrective or perfective. Corrective 2004 TE prescribes a course of action to address an existing or predicted 2005 anomaly. Perfective TE prescribes a course of action to evolve and 2006 improve network performance even when no anomalies are evident. 2008 Descriptive traffic engineering, on the other hand, characterizes the 2009 state of the network and assesses the impact of various policies 2010 without recommending any particular course of action. 2012 5.6 Open-Loop Versus Closed-Loop 2014 Open-loop traffic engineering control is where control action does 2015 not use feedback information from the current network state. The 2016 control action may use its own local information for accounting 2017 purposes, however. 2019 Closed-loop traffic engineering control is where control action 2020 utilizes feedback information from the network state. The feedback 2021 information may be in the form of historical information or current 2022 measurement. 2024 5.7 Tactical vs Strategic 2026 Tactical traffic engineering aims to address specific performance 2027 problems (such as hot-spots) that occur in the network from a 2028 tactical perspective, without consideration of overall strategic 2029 imperatives. Without proper planning and insights, tactical TE tends 2030 to be ad hoc in nature. 2032 Strategic traffic engineering approaches the TE problem from a more 2033 organized and systematic perspective, taking into consideration the 2034 immediate and longer term consequences of specific policies and 2035 actions. 2037 6.0 Recommendations for Internet Traffic Engineering 2039 This section describes high level recommendations for traffic 2040 engineering in the Internet. These recommendations are presented in 2041 general terms because this is a framework document. 2043 The recommendations describe the capabilities needed to solve a 2044 traffic engineering problem or to achieve a traffic engineering 2045 objective. Broadly speaking, these recommendations can be categorized 2046 as either functional and non-functional recommendations. 2048 Functional recommendations for Internet traffic engineering describe 2049 the functions that a traffic engineering system should perform. These 2050 functions are needed to realize traffic engineering objectives by 2051 addressing traffic engineering problems. 2053 Non-functional recommendations for Internet traffic engineering 2054 relate to the quality attributes or state characteristics of a 2055 traffic engineering system. These recommendations may contain 2056 conflicting assertions and may sometimes be difficult to quantify 2057 precisely. 2059 6.1 Generic Non-functional Recommendations 2061 The generic non-functional recommendations for Internet traffic 2062 engineering include: usability, automation, scalability, stability, 2063 visibility, simplicity, efficiency, reliability, correctness, 2064 maintainability, extensibility, interoperability, and security. In a 2065 given context, some of these recommendations may be critical while 2066 others may be optional. Therefore, prioritization may be required 2067 during the development phase of a traffic engineering system (or 2068 components thereof) to tailor it to a specific operational context. 2070 In the following paragraphs, some of the aspects of the non- 2071 functional recommendations for Internet traffic engineering are 2072 summarized. 2074 Usability: Usability is a human factor aspect of traffic engineering 2075 systems. Usability refers to the ease with which a traffic 2076 engineering system can be deployed and operated. In general, it is 2077 desirable to have a TE system that can be readily deployed in an 2078 existing network. It is also desirable to have a TE system that is 2079 easy to operate and maintain. 2081 Automation: Whenever feasible, a traffic engineering system should 2082 automate as many traffic engineering functions as possible to 2083 minimize the amount of human effort needed to control and analyze 2084 operational networks. Automation is particularly imperative in large 2085 scale public networks because of the high cost of the human aspects 2086 of network operations and the high risk of network problems caused by 2087 human errors. Automation may entail the incorporation of automatic 2088 feedback and intelligence into some components of the traffic 2089 engineering system. 2091 Scalability: Contemporary public networks are growing very fast with 2092 respect to network size and traffic volume. Therefore, a TE system 2093 should be scalable to remain applicable as the network evolves. In 2094 particular, a TE system should remain functional as the network 2095 expands with regard to the number of routers and links, and with 2096 respect to the traffic volume. A TE system should have a scalable 2097 architecture, should not adversely impair other functions and 2098 processes in a network element, and should not consume too much 2099 network resources when collecting and distributing state information 2100 or when exerting control. 2102 Stability: Stability is a very important consideration in traffic 2103 engineering systems that respond to changes in the state of the 2104 network. State-dependent traffic engineering methodologies typically 2105 mandate a tradeoff between responsiveness and stability. It is 2106 strongly recommended that when tradeoffs are warranted between 2107 responsiveness and stability, that the tradeoff should be made in 2108 favor of stability (especially in public IP backbone networks). 2110 Flexibility: A TE system should be flexible to allow for changes in 2111 optimization policy. In particular, a TE system should provide 2112 sufficient configuration options so that a network administrator can 2113 tailor the TE system to a particular environment. It may also be 2114 desirable to have both online and offline TE subsystems which can be 2115 independently enabled and disabled. TE systems that are used in 2116 multi-class networks should also have options to support class based 2117 performance evaluation and optimization. 2119 Visibility: As part of the TE system, mechanisms should exist to 2120 collect statistics from the network and to analyze these statistics 2121 to determine how well the network is functioning. Derived statistics 2122 such as traffic matrices, link utilization, latency, packet loss, and 2123 other performance measures of interest which are determined from 2124 network measurements can be used as indicators of prevailing network 2125 conditions. Other examples of status information which should be 2126 observed include existing functional routing information 2127 (additionally, in the context of MPLS existing LSP routes), etc. 2129 Simplicity: Generally, a TE system should be as simple as possible. 2130 More importantly, the TE system should be relatively easy to use 2131 (i.e., clean, convenient, and intuitive user interfaces). Simplicity 2132 in user interface does not necessarily imply that the TE system will 2133 use naive algorithms. When complex algorithms and internal structures 2134 are used, such complexities should be hidden as much as possible from 2135 the network administrator through the user interface. 2137 Interoperability: Whenever feasible, traffic engineering systems and 2138 their components should be developed with open standards based 2139 interfaces to allow interoperation with other systems and components. 2141 Security: Security is a critical consideration in traffic engineering 2142 systems. Such traffic engineering systems typically exert control 2143 over certain functional aspects of the network to achieve the desired 2144 performance objectives. Therefore, adequate measures must be taken to 2145 safeguard the integrity of the traffic engineering system. Adequate 2146 measures must also be taken to protect the network from 2147 vulnerabilities that originate from security breaches and other 2148 impairments within the traffic engineering system. 2150 The remainder of this section will focus on some of the high level 2151 functional recommendations for traffic engineering. 2153 6.2 Routing Recommendations 2155 Routing control is a significant aspect of Internet traffic 2156 engineering. Routing impacts many of the key performance measures 2157 associated with networks, such as throughput, delay, and utilization. 2158 Generally, it is very difficult to provide good service quality in a 2159 wide area network without effective routing control. A desirable 2160 routing system is one that takes traffic characteristics and network 2161 constraints into account during route selection while maintaining 2162 stability. 2164 Traditional shortest path first (SPF) interior gateway protocols are 2165 based on shortest path algorithms and have limited control 2166 capabilities for traffic engineering [RFC-2702, AWD2]. These 2167 limitations include : 2169 1. The well known issues with pure SPF protocols, which 2170 do not take network constraints and traffic characteristics 2171 into account during route selection. For example, since IGPs 2172 always use the shortest paths (based on administratively 2173 assigned link metrics) to forward traffic, load sharing cannot 2174 be accomplished among paths of different costs. Using shortest 2175 paths to forward traffic conserves network resources, but may 2176 cause the following problems: 1) If traffic from a source to a 2177 destination exceeds the capacity of a link along the shortest 2178 path, the link (hence the shortest path) becomes congested while 2179 a longer path between these two nodes may be under-utilized; 2180 2) the shortest paths from different sources can overlap at some 2181 links. If the total traffic from the sources exceeds the 2182 capacity of any of these links, congestion will occur. Problems 2183 can also occur because traffic demand changes over time but 2184 network topology and routing configuration cannot be changed as 2185 rapidly. This causes the network topology and routing 2186 configuration to become sub-optimal over time, which may result 2187 in persistent congestion problems. 2189 2. The Equal-Cost Multi-Path (ECMP) capability of SPF IGPs supports 2190 sharing of traffic among equal cost paths between two nodes. 2191 However, ECMP attempts to divide the traffic as equally as 2192 possible among the equal cost shortest paths. Generally, ECMP 2193 does not support configurable load sharing ratios among equal 2194 cost paths. The result is that one of the paths may carry 2195 significantly more traffic than other paths because it 2196 may also carry traffic from other sources. This situation can 2197 result in congestion along the path that carries more traffic. 2199 3. Modifying IGP metrics to control traffic routing tends to 2200 have network-wide effect. Consequently, undesirable and 2201 unanticipated traffic shifts can be triggered as a result. 2203 Because of these limitations, new capabilities are needed to enhance 2204 the routing function in IP networks. Some of these capabilities have 2205 been described elsewhere and are summarized below. 2207 Constraint-based routing is desirable to evolve the routing 2208 architecture of IP networks, especially public IP backbones with 2209 complex topologies [RFC-2702]. Constraint-based routing computes 2210 routes to fulfill requirements subject to constraints. Constraints 2211 may include bandwidth, hop count, delay, and administrative policy 2212 instruments such as resource class attributes [RFC-2702, RFC-2386]. 2213 This makes it possible to select routes that satisfy a given set of 2214 requirements subject to network and administrative policy 2215 constraints. Routes computed through constraint-based routing are not 2216 necessarily the shortest paths. Constraint-based routing works best 2217 with path oriented technologies that support explicit routing, such 2218 as MPLS. 2220 Constraint-based routing can also be used as a way to redistribute 2221 traffic onto the infrastructure (even for best effort traffic). For 2222 example, if the bandwidth requirements for path selection and 2223 reservable bandwidth attributes of network links are appropriately 2224 defined and configured, then congestion problems caused by uneven 2225 traffic distribution may be avoided or reduced. In this way, the 2226 performance and efficiency of the network can be improved. 2228 A number of enhancements are needed to conventional link state IGPs, 2229 such as OSPF and IS-IS, to allow them to distribute additional state 2230 information required for constraint-based routing. These extensions 2231 to OSPF were described in [KATZ] and to IS-IS in [SMIT]. 2232 Essentially, these enhancements require the propagation of additional 2233 information in link state advertisements. Specifically, in addition 2234 to normal link-state information, an enhanced IGP is required to 2235 propagate topology state information needed for constraint-based 2236 routing. Some of the additional topology state information include 2237 link attributes such as reservable bandwidth and link resource class 2238 attribute (an administratively specified property of the link). The 2239 resource class attribute concept was defined in [RFC-2702]. The 2240 additional topology state information is carried in new TLVs and 2241 sub-TLVs in IS-IS, or in the Opaque LSA in OSPF [SMIT, KATZ]. 2243 An enhanced link-state IGP may flood information more frequently than 2244 a normal IGP. This is because even without changes in topology, 2245 changes in reservable bandwidth or link affinity can trigger the 2246 enhanced IGP to initiate flooding. A tradeoff is typically required 2247 between the timeliness of the information flooded and the flooding 2248 frequency to avoid excessive consumption of link bandwidth and 2249 computational resources, and more importantly, to avoid instability. 2251 In a TE system, it is also desirable for the routing subsystem to 2252 make the load splitting ratio among multiple paths (with equal cost 2253 or different cost) configurable. This capability gives network 2254 administrators more flexibility in the control of traffic 2255 distribution across the network. It can be very useful for 2256 avoiding/relieving congestion in certain situations. Examples can be 2257 found in [XIAO]. 2259 The routing system should also have the capability to control the 2260 routes of subsets of traffic without affecting the routes of other 2261 traffic if sufficient resources exist for this purpose. This 2262 capability allows a more refined control over the distribution of 2263 traffic across the network. For example, the ability to move traffic 2264 from a source to a destination away from its original path to another 2265 path (without affecting other traffic paths) allows traffic to be 2266 moved from resource-poor network segments to resource-rich segments. 2267 Path oriented technologies such as MPLS inherently support this 2268 capability as discussed in [AWD2]. 2270 Additionally, the routing subsystem should be able to select 2271 different paths for different classes of traffic (or for different 2272 traffic behavior aggregates) if the network supports multiple classes 2273 of service (different behavior aggregates). 2275 6.3 Traffic Mapping Recommendations 2277 Traffic mapping pertains to the assignment of traffic workload onto 2278 pre-established paths to meet certain requirements. Thus, while 2279 constraint-based routing deals with path selection, traffic mapping 2280 deals with the assignment of traffic to established paths which may 2281 have been selected by constraint-based routing or by some other 2282 means. Traffic mapping can be performed by time-dependent or state- 2283 dependent mechanisms, as described in Section 5.1. 2285 An important aspect of the traffic mapping function is the ability to 2286 establish multiple paths between an originating node and a 2287 destination node, and the capability to distribute the traffic 2288 between the two nodes across the paths according to some policies. A 2289 pre-condition for this scheme is the existence of flexible mechanisms 2290 to partition traffic and then assign the traffic partitions onto the 2291 parallel paths. This requirement was noted in [RFC-2702]. When 2292 traffic is assigned to multiple parallel paths, it is recommended 2293 that special care should be taken to ensure proper ordering of 2294 packets belonging to the same application (or micro-flow) at the 2295 destination node of the parallel paths. 2297 As a general rule, mechanisms that perform the traffic mapping 2298 functions should aim to map the traffic onto the network 2299 infrastructure to minimize congestion. If the total traffic load 2300 cannot be accommodated, or if the routing and mapping functions 2301 cannot react fast enough to changing traffic conditions, then a 2302 traffic mapping system may rely on short time scale congestion 2303 control mechanisms (such as queue management, scheduling, etc) to 2304 mitigate congestion. Thus, mechanisms that perform the traffic 2305 mapping functions should complement existing congestion control 2306 mechanisms. In an operational network, it is generally desirable to 2307 map the traffic onto the infrastructure such that intra-class and 2308 inter-class resource contention are minimized. 2310 When traffic mapping techniques that depend on dynamic state feedback 2311 (e.g. MATE and such like) are used, special care must be taken to 2312 guarantee network stability. 2314 6.4 Measurement Recommendations 2316 The importance of measurement in traffic engineering has been 2317 discussed throughout this document. Mechanisms should be provided to 2318 measure and collect statistics from the network to support the 2319 traffic engineering function. Additional capabilities may be needed 2320 to help in the analysis of the statistics. The actions of these 2321 mechanisms should not adversely affect the accuracy and integrity of 2322 the statistics collected. The mechanisms for statistical data 2323 acquisition should also be able to scale as the network evolves. 2325 Traffic statistics may be classified according to long-term or 2326 short-term time scales. Long-term time scale traffic statistics are 2327 very useful for traffic engineering. Long-term time scale traffic 2328 statistics may capture or reflect periodicity in network workload 2329 (such as hourly, daily, and weekly variations in traffic profiles) as 2330 well as traffic trends. Aspects of the monitored traffic statistics 2331 may also depict class of service characteristics for a network 2332 supporting multiple classes of service. Analysis of the long-term 2333 traffic statistics MAY yield secondary statistics such as busy hour 2334 characteristics, traffic growth patterns, persistent congestion 2335 problems, hot-spot, and imbalances in link utilization caused by 2336 routing anomalies. 2338 A mechanism for constructing traffic matrices for both long-term and 2339 short-term traffic statistics should be in place. In multi-service IP 2340 networks, the traffic matrices may be constructed for different 2341 service classes. Each element of a traffic matrix represents a 2342 statistic of traffic flow between a pair of abstract nodes. An 2343 abstract node may represent a router, a collection of routers, or a 2344 site in a VPN. 2346 Measured traffic statistics should provide reasonable and reliable 2347 indicators of the current state of the network on the short-term 2348 scale. Some short term traffic statistics may reflect link 2349 utilization and link congestion status. Examples of congestion 2350 indicators include excessive packet delay, packet loss, and high 2351 resource utilization. Examples of mechanisms for distributing this 2352 kind of information include SNMP, probing techniques, FTP, IGP link 2353 state advertisements, etc. 2355 6.5 Network Survivability 2357 Network survivability refers to the capability of a network to 2358 maintain service continuity in the presence of faults. This can be 2359 accomplished by promptly recovering from network impairments and 2360 maintaining the required QoS for existing services after recovery. 2361 Survivability has become an issue of great concern within the 2362 Internet community due to the increasing demands to carry mission 2363 critical traffic, real-time traffic, and other high priority traffic 2364 over the Internet. Survivability can be addressed at the device level 2365 by developing network elements that are more reliable; and at the 2366 network level by incorporating redundancy into the architecture, 2367 design, and operation of networks. It is recommended that a 2368 philosophy of robustness and survivability should be adopted in the 2369 architecture, design, and operation of traffic engineering that 2370 control IP networks (especially public IP networks). Because 2371 different contexts may demand different levels of survivability, the 2372 mechanisms developed to support network survivability should be 2373 flexible so that they can be tailored to different needs. 2375 Failure protection and restoration capabilities have become available 2376 from multiple layers as network technologies have continued to 2377 improve. At the bottom of the layered stack, optical networks are now 2378 capable of providing dynamic ring and mesh restoration functionality 2379 at the wavelength level as well as traditional protection 2380 functionality. At the SONET/SDH layer survivability capability is 2381 provided with Automatic Protection Switching (APS) as well as self- 2382 healing ring and mesh architectures. Similar functionality is 2383 provided by layer 2 technologies such as ATM (generally with slower 2384 mean restoration times). Rerouting is traditionally used at the IP 2385 layer to restore service following link and node outages. Rerouting 2386 at the IP layer occurs after a period of routing convergence which 2387 may require seconds to minutes to complete. Some new developments in 2388 the MPLS context make it possible to achieve recovery at the IP layer 2389 prior to convergence [SHAR]. 2391 To support advanced survivability requirements, path-oriented 2392 technologies such a MPLS can be used to enhance the survivability of 2393 IP networks in a potentially cost effective manner. The advantages of 2394 path oriented technologies such as MPLS for IP restoration becomes 2395 even more evident when class based protection and restoration 2396 capabilities are required. 2398 Recently, a common suite of control plane protocols has been proposed 2399 for both MPLS and optical transport networks under the acronym 2400 Multi-protocol Lambda Switching [AWD1]. This new paradigm of Multi- 2401 protocol Lambda Switching will support even more sophisticated mesh 2402 restoration capabilities at the optical layer for the emerging IP 2403 over WDM network architectures. 2405 Another important aspect regarding multi-layer survivability is that 2406 technologies at different layers provide protection and restoration 2407 capabilities at different temporal granularities (in terms of time 2408 scales) and at different bandwidth granularity (from packet-level to 2409 wavelength level). Protection and restoration capabilities can also 2410 be sensitive to different service classes and different network 2411 utility models. 2413 The impact of service outages varies significantly for different 2414 service classes depending upon the effective duration of the outage. 2415 The duration of an outage can vary from milliseconds (with minor 2416 service impact) to seconds (with possible call drops for IP telephony 2417 and session time-outs for connection oriented transactions) to 2418 minutes and hours (with potentially considerable social and business 2419 impact). 2421 Coordinating different protection and restoration capabilities across 2422 multiple layers in a cohesive manner to ensure network survivability 2423 is maintained at reasonable cost is a challenging task. Protection 2424 and restoration coordination across layers may not always be 2425 feasible, because networks at different layers may belong to 2426 different administrative domains. 2428 The following paragraphs present some of the general recommendations 2429 for protection and restoration coordination. 2431 - Protection and restoration capabilities from different layers 2432 should be coordinated whenever feasible and appropriate to 2433 provide network survivability in a flexible and cost effective 2434 manner. Minimization of function duplication across layers is 2435 one way to achieve the coordination. Escalation of alarms and 2436 other fault indicators from lower to higher layers may also 2437 be performed in a coordinated manner. A temporal order of 2438 restoration trigger timing at different layers is another way 2439 to coordinate multi-layer protection/restoration. 2441 - Spare capacity at higher layers is often regarded as working 2442 traffic at lower layers. Placing protection/restoration 2443 functions in many layers may increase redundancy and robustness, 2444 but it should not result in significant and avoidable 2445 inefficiencies in network resource utilization. 2447 - It is generally desirable to have protection and restoration 2448 schemes that are bandwidth efficient. 2450 - Failure notification throughout the network should be timely 2451 and reliable. 2453 - Alarms and other fault monitoring and reporting capabilities 2454 should be provided at appropriate layers. 2456 6.5.1 Survivability in MPLS Based Networks 2458 MPLS is an important emerging technology that enhances IP networks in 2459 terms of features, capabilities, and services. Because MPLS is path- 2460 oriented it can potentially provide faster and more predictable 2461 protection and restoration capabilities than conventional hop by hop 2462 routed IP systems. This subsection describes some of the basic 2463 aspects and recommendations for MPLS networks regarding protection 2464 and restoration. See [SHAR] for a more comprehensive discussion on 2465 MPLS based recovery. 2467 Protection types for MPLS networks can be categorized as link 2468 protection, node protection, path protection, and segment protection. 2470 - Link Protection: The objective for link protection is to protect 2471 an LSP from a given link failure. Under link protection, the path 2472 of the protection or backup LSP (the secondary LSP) is disjoint 2473 from the path of the working or operational LSP at the particular 2474 link over which protection is required. When the protected link 2475 fails, traffic on the working LSP is switched over to the 2476 protection LSP at the head-end of the failed link. This is a local 2477 repair method which can be fast. It might be more appropriate in 2478 situations where some network elements along a given path are 2479 less reliable than others. 2481 - Node Protection: The objective of LSP node protection is to protect 2482 an LSP from a given node failure. Under node protection, the path 2483 of the protection LSP is disjoint from the path of the working LSP 2484 at the particular node to be protected. The secondary path is 2485 also disjoint from the primary path at all links associated with 2486 the node to be protected. When the node fails, traffic on the 2487 working LSP is switched over to the protection LSP at the upstream 2488 LSR directly connected to the failed node. 2490 - Path Protection: The goal of LSP path protection is to protect an 2491 LSP from failure at any point along its routed path. Under path 2492 protection, the path of the protection LSP is completely disjoint 2493 from the path of the working LSP. The advantage of path protection 2494 is that the backup LSP protects the working LSP from all possible 2495 link and node failures along the path, except for failures that 2496 might occur at the ingress and egress LSRs, or for correlated 2497 failures that might impact both working and backup paths 2498 simultaneously. Additionally, since the path selection is 2499 end-to-end, path protection might be more efficient in terms of 2500 resource usage than link or node protection. However, path 2501 protection may be slower than link and node protection in general. 2503 - Segment Protection: An MPLS domain may be partitioned into multiple 2504 protection domains whereby a failure in a protection domain is 2505 rectified within that domain. In cases where an LSP traverses 2506 multiple protection domains, a protection mechanism within a domain 2507 only needs to protect the segment of the LSP that lies within the 2508 domain. Segment protection will generally be faster than path 2509 protection because recovery generally occurs closer to the fault. 2511 6.5.2 Protection Option 2513 Another issue to consider is the concept of protection options. The 2514 protection option uses the notation m:n protection where m is the 2515 number of protection LSPs used to protect n working LSPs. Feasible 2516 protection options follow. 2518 - 1:1: one working LSP is protected/restored by one protection LSP. 2520 - 1:n: one protection LSP is used to protect/restore n working LSPs. 2522 - n:1: one working LSP is protected/restored by n protection LSPs, 2523 possibly with configurable load splitting ratio. When more than 2524 one protection LSP is used, it may be desirable to share the 2525 traffic across the protection LSPs when the working LSP fails to 2526 satisfy the bandwidth requirement of the traffic trunk associated 2527 with the working LSP. This may be especially useful when it is 2528 not feasible to find one path that can satisfy the bandwidth 2529 requirement of the primary LSP. 2531 - 1+1: traffic is sent concurrently on both the working LSP and the 2532 protection LSP. In this case, the egress LSR selects one of the two 2533 LSPs based on a local traffic integrity decision process, which 2534 compares the traffic received from both the working and the 2535 protection LSP and identifies discrepancies. It is unlikely that 2536 this option would be used extensively in IP networks due to its 2537 resource utilization inefficiency. However, if bandwidth becomes 2538 plentiful and cheap, then this option might become quite viable and 2539 attractive in IP networks. 2541 6.6 Traffic Engineering in Diffserv Environments 2543 This section provides an overview of the traffic engineering features 2544 and recommendations that are specifically pertinent to Differentiated 2545 Services (Diffserv) [RFC-2475] capable IP networks. 2547 Increasing requirements to support multiple classes of traffic, such 2548 as best effort and mission critical data, in the Internet calls for 2549 IP networks to differentiate traffic according to some criteria, and 2550 to accord preferential treatment to certain types of traffic. Large 2551 numbers of flows can be aggregated into a few behavior aggregates 2552 based on some criteria in terms of common performance requirements in 2553 terms of packet loss ratio, delay, and jitter; or in terms of common 2554 fields within the IP packet headers. 2556 As Diffserv evolves and becomes deployed in operational networks, 2557 traffic engineering will be critical to ensuring that SLAs defined 2558 within a given Diffserv service model are met. Classes of service 2559 (CoS) can be supported in a Diffserv environment by concatenating 2560 per-hop behaviors (PHBs) along the routing path, using service 2561 provisioning mechanisms, and by appropriately configuring edge 2562 functionality such as traffic classification, marking, policing, and 2563 shaping. PHB is the forwarding behavior that a packet receives at a 2564 DS node (a Diffserv-compliant node). This is accomplished by means of 2565 buffer management and packet scheduling mechanisms. In this context, 2566 packets belonging to a class are those that are members of a 2567 corresponding ordering aggregate. 2569 Traffic engineering can be used as a compliment to Diffserv 2570 mechanisms to improve utilization of network resources, but not as a 2571 necessary element in general. When traffic engineering is used, it 2572 can be operated on an aggregated basis across all service classes 2573 [MPLS-DIFF] or on a per service class basis. The former is used to 2574 provide better distribution of the aggregate traffic load over the 2575 network resources. (See [MPLS_DIFF] for detailed mechanisms to 2576 support aggregate traffic engineering.) The latter case is discussed 2577 below since it is specific to the Diffserv environment, with so 2578 called Diffserv-aware traffic engineering [DIFF_TE]. 2580 For some Diffserv networks, it may be desirable to control the 2581 performance of some service classes by enforcing certain 2582 relationships between the traffic workload contributed by each 2583 service class and the amount of network resources allocated or 2584 provisioned for that service class. Such relationships between 2585 demand and resource allocation can be enforced using a combination 2586 of, for example: (1) traffic engineering mechanisms on a per service 2587 class basis that enforce the desired relationship between the amount 2588 of traffic contributed by a given service class and the resources 2589 allocated to that class and (2) mechanisms that dynamically adjust 2590 the resources allocated to a given service class to relate to the 2591 amount of traffic contributed by that service class. 2593 It may also be desirable to limit the performance impact of high 2594 priority traffic on relatively low priority traffic. This can be 2595 achieved by, for example, controlling the percentage of high priority 2596 traffic that is routed through a given link. Another way to 2597 accomplish this is to increase link capacities appropriately so that 2598 lower priority traffic can still enjoy adequate service quality. When 2599 the ratio of traffic workload contributed by different service 2600 classes vary significantly from router to router, it may not suffice 2601 to rely exclusively on conventional IGP routing protocols or on 2602 traffic engineering mechanisms that are insensitive to different 2603 service classes. Instead, it may be desirable to perform traffic 2604 engineering, especially routing control and mapping functions, on a 2605 per service class basis. One way to accomplish this in a domain that 2606 supports both MPLS and Diffserv is to define class specific LSPs and 2607 to map traffic from each class onto one or more LSPs that correspond 2608 to that service class. An LSP corresponding to a given service class 2609 can then be routed and protected/restored in a class dependent 2610 manner, according to specific policies. 2612 Performing traffic engineering on a per class basis requires certain 2613 per-class parameters to be propagated via IGP link state 2614 advertisements (LSAs). Note that it is common to have some classes to 2615 share some aggregate constraint (e.g. maximum bandwidth requirement) 2616 without enforcing the constraint on each individual class. These 2617 classes then might be grouped into a class-type and per-class-type 2618 parameters might be propagated instead to improve the scalability of 2619 IGP LSAs. It also allows better bandwidth sharing between classes in 2620 the same class-type. A class-type is a set of classes that satisfy 2621 the following two conditions: 2623 1) Classes in the same class-type have common aggregate requirements 2624 to satisfy required performance levels. 2626 2) There is no requirement to be enforced at the level of individual 2627 class in the class-type. Note that it is still possible, 2628 nevertheless, to implement some priority policies for classes in the 2629 same class-type to permit preferential access to the class-type 2630 bandwidth through the use of preemption priorities. 2632 An example of the class-type can be a low-loss class-type that 2633 includes both AF1-based and AF2-based Ordering Aggregates. With such 2634 a class-type, one may implement some priority policy which assigns 2635 higher preemption priority to AF1-based traffic trunks over AF2-based 2636 ones, vice versa, or the same priority. 2638 See [DIFF-TE] for detailed requirements on Diffserv-aware traffic 2639 engineering. 2641 6.7 Network Controllability 2643 Off-line (and on-line) traffic engineering considerations would be of 2644 limited utility if the network could not be controlled effectively to 2645 implement the results of TE decisions and to achieve desired network 2646 performance objectives. Capacity augmentation is a coarse grained 2647 solution to traffic engineering issues. However, it is simple and may 2648 be advantageous if bandwidth is abundant and cheap or if the current 2649 or expected network workload demands it. However, bandwidth is not 2650 always abundant and cheap, and the workload may not always demand 2651 additional capacity. Adjustments of administrative weights and other 2652 parameters associated with routing protocols provide finer grained 2653 control, but is difficult to use and imprecise because of the routing 2654 interactions that occur across the network. In certain network 2655 contexts, more flexible, finer grained approaches which provide more 2656 precise control over the mapping of traffic to routes and over the 2657 selection and placement of routes may be appropriate and useful. 2659 Control mechanisms can be manual (e.g. administrative configuration), 2660 partially-automated (e.g. scripts) or fully-automated (e.g. policy 2661 based management systems). Automated mechanisms are particularly 2662 required in large scale networks. Multi-vendor interoperability can 2663 be facilitated by developing and deploying standardized management 2664 systems (e.g. standard MIBs) and policies (PIBs) to support the 2665 control functions required to address traffic engineering objectives 2666 such as load distribution and protection/restoration. 2668 Network control functions should be secure, reliable, and stable as 2669 these are often needed to operate correctly in times of network 2670 impairments (e.g. during network congestion or security attacks). 2672 7.0 Inter-Domain Considerations 2674 Inter-domain traffic engineering is concerned with the performance 2675 optimization for traffic that originates in one administrative domain 2676 and terminates in a different one. 2678 Traffic exchange between autonomous systems in the Internet occurs 2679 through exterior gateway protocols. Currently, BGP-4 [BGP4] is the 2680 standard exterior gateway protocol for the Internet. BGP-4 provides 2681 a number of attributes (e.g. local preference, AS path, and MED) and 2682 capabilities (e.g. route filtering) that can be used for inter-domain 2683 traffic engineering. These mechanisms are generally effective, but 2684 they are usually applied in a trial-and-error fashion. A systematic 2685 approach for inter-domain traffic engineering is yet to be devised. 2687 Inter-domain traffic engineering is inherently more difficult than 2688 intra-domain TE under the current Internet architecture. The reasons 2689 for this are both technical and administrative. Technically, while 2690 topology and link state information are helpful for mapping traffic 2691 more effectively, BGP does not propagate such information across 2692 domain boundaries for stability and scalability reasons. 2693 Administratively, there are differences in operating costs and 2694 network capacities between domains. Generally, what may be considered 2695 a good solution in one domain may not necessarily be a good solution 2696 in another domain. Moreover, it would generally be considered 2697 inadvisable for one domain to permit another domain to influence the 2698 routing and management of traffic in its network. 2700 MPLS TE-tunnels (explicit LSPs) can potentially add a degree of 2701 flexibility in the selection of exit points for inter-domain routing. 2702 The concept of relative and absolute metrics can be applied to this 2703 purpose. The idea is that if BGP attributes are defined such that the 2704 BGP decision process depends on IGP metrics to select exit points for 2705 inter-domain traffic, then some inter-domain traffic destined to a 2706 given peer network can be made to prefer a specific exit point by 2707 establishing a TE-tunnel between the router making the selection to 2708 the peering point via a TE-tunnel and assigning the TE-tunnel a 2709 metric which is smaller than the IGP cost to all other peering 2710 points. If a peer accepts and processes MEDs, then a similar MPLS 2711 TE-tunnel based scheme can be applied to cause certain entrance 2712 points to be preferred by setting MED to be an IGP cost, which has 2713 been modified by the tunnel metric. 2715 Similar to intra-domain TE, inter-domain TE is best accomplished when 2716 a traffic matrix can be derived to depict the volume of traffic from 2717 one autonomous system to another. 2719 Generally, redistribution of inter-domain traffic requires 2720 coordination between peering partners. An export policy in one domain 2721 that results in load redistribution across peer points with another 2722 domain can significantly affect the local traffic matrix inside the 2723 domain of the peering partner. This, in turn, will affect the intra- 2724 domain TE due to changes in the spatial distribution traffic. 2725 Therefore, it is mutually beneficial for peering partners to 2726 coordinate with each other before attempting any policy changes that 2727 may result in significant shifts in inter-domain traffic. In certain 2728 contexts, this coordination can be quite challenging due to technical 2729 and non- technical reasons. 2731 It is a matter of speculation as to whether MPLS, or similar 2732 technologies, can be extended to allow selection of constrained paths 2733 across domain boundaries. 2735 8.0 Overview of Contemporary TE Practices in Operational IP Networks 2737 This section provides an overview of some contemporary traffic 2738 engineering practices in IP networks. The focus is primarily on the 2739 aspects that pertain to the control of the routing function in 2740 operational contexts. The intent here is to provide an overview of 2741 the commonly used practices. The discussion is not intended to be 2742 exhaustive. 2744 Currently, service providers apply many of the traffic engineering 2745 mechanisms discussed in this document to optimize the performance of 2746 their IP networks. These techniques include capacity planning for 2747 long time scales, routing control using IGP metrics and MPLS for 2748 medium time scales, the overlay model also for medium time scales, 2749 and traffic management mechanisms for short time scale. 2751 When a service provider plans to build an IP network, or expand the 2752 capacity of an existing network, effective capacity planning should 2753 be an important component of the process. Such plans may take the 2754 following aspects into account: location of new nodes if any, 2755 existing and predicted traffic patterns, costs, link capacity, 2756 topology, routing design, and survivability. 2758 Performance optimization of operational networks is usually an 2759 ongoing process in which traffic statistics, performance parameters, 2760 and fault indicators are continually collected from the network. 2761 These empirical data are then analyzed and used to trigger various 2762 traffic engineering mechanisms. For example, IGP parameters, e.g., 2763 OSPF or IS-IS metrics, can be adjusted based on manual computations 2764 or based on the output of some traffic engineering support tools. 2765 Such tools may use the following as input the: traffic matrix, 2766 network topology, and network performance objective(s). Tools that 2767 perform what-if analysis can also be used to assist the TE process by 2768 allowing various scenarios to be reviewed before a new set of 2769 configurations are implemented in the operational network. 2771 The overlay model (IP over ATM or IP over Frame relay) is another 2772 approach which is commonly used in practice [AWD2]. The IP over ATM 2773 technique is no longer viewed favorably due to recent advances in 2774 MPLS and router hardware technology. 2776 Deployment of MPLS for traffic engineering applications has commenced 2777 in some service provider networks. One operational scenario is to 2778 deploy MPLS in conjunction with an IGP (IS-IS-TE or OSPF-TE) that 2779 supports the traffic engineering extensions, in conjunction with 2780 constraint-based routing for explicit route computations, and a 2781 signaling protocol (e.g. RSVP-TE or CRLDP) for LSP instantiation. 2783 In contemporary MPLS traffic engineering contexts, network 2784 administrators specify and configure link attributes and resource 2785 constraints such as maximum reservable bandwidth and resource class 2786 attributes for links (interfaces) within the MPLS domain. A link 2787 state protocol that supports TE extensions (IS-IS-TE or OSPF-TE) is 2788 used to propagate information about network topology and link 2789 attribute to all routers in the routing area. Network administrators 2790 also specify all the LSPs that are to originate each router. For each 2791 LSP, the network administrator specifies the destination node and the 2792 attributes of the LSP which indicate the requirements that to be 2793 satisfied during the path selection process. Each router then uses a 2794 local constraint-based routing process to compute explicit paths for 2795 all LSPs originating from it. Subsequently, a signaling protocol is 2796 used to instantiate the LSPs. By assigning proper bandwidth values to 2797 links and LSPs, congestion caused by uneven traffic distribution can 2798 generally be avoided or mitigated. 2800 The bandwidth attributes of LSPs used for traffic engineering can be 2801 updated periodically. The basic concept is that the bandwidth 2802 assigned to an LSP should relate in some manner to the bandwidth 2803 requirements of traffic that actually flows through the LSP. The 2804 traffic attribute of an LSP can be modified to accommodate traffic 2805 growth and persistent traffic shifts. If network congestion occurs 2806 due to some unexpected events, existing LSPs can be rerouted to 2807 alleviate the situation or network administrator can configure new 2808 LSPs to divert some traffic to alternative paths. The reservable 2809 bandwidth of the congested links can also be reduced to force some 2810 LSPs to be rerouted to other paths. 2812 In an MPLS domain, a traffic matrix can also be estimated by 2813 monitoring the traffic on LSPs. Such traffic statistics can be used 2814 for a variety of purposes including network planning and network 2815 optimization. Current practice suggests that deploying an MPLS 2816 network consisting of hundreds of routers and thousands of LSPs is 2817 feasible. In summary, recent deployment experience suggests that MPLS 2818 approach is very effective for traffic engineering in IP networks 2819 [XIAO]. 2821 9.0 Conclusion 2823 This document described a framework for traffic engineering in the 2824 Internet. It presented an overview of some of the basic issues 2825 surrounding traffic engineering in IP networks. The context of TE was 2826 described, a TE process models and a taxonomy of TE styles were 2827 presented. A brief historical review of pertinent developments 2828 related to traffic engineering was provided. A survey of contemporary 2829 TE techniques in operational networks was presented. Additionally, 2830 the document specified a set of generic requirements, 2831 recommendations, and options for Internet traffic engineering. 2833 10.0 Security Considerations 2835 This document does not introduce new security issues. 2837 11.0 Acknowledgments 2839 The authors would like to thank Jim Boyle for inputs on the 2840 recommendations section, Francois Le Faucheur for inputs on Diffserv 2841 aspects, Blaine Christian for inputs on measurement, Gerald Ash for 2842 inputs on routing in telephone networks and for text on event- 2843 dependent TE methods , and Steven Wright for inputs on network 2844 controllability. Special thanks to Randy Bush for proposing the TE 2845 taxonomy based on "tactical vs strategic" methods. The subsection 2846 describing an "Overview of ITU Activities Related to Traffic 2847 Engineering" was adapted from a contribution by Waisum Lai. Useful 2848 feedback and pointers to relevant materials were provided by J. Noel 2849 Chiappa. Additional comments were provided by Glenn Grotefeld during 2850 the working last call process. Finally, the authors would like to 2851 thank Ed Kern, the TEWG co-chair, for his comments and support. 2853 12.0 References 2855 [ASH1] J. Ash, M. Girish, E. Gray, B. Jamoussi, G. Wright, 2856 "Applicability Statement for CR-LDP," Work in Progress, July 2000. 2858 [ASH2] J. Ash, Dynamic Routing in Telecommunications Networks, McGraw 2859 Hill, 1998 2861 [ASH3] J. Ash, "TE & QoS Methods for IP-, ATM-, & TDM-Based 2862 Networks," Work in Progress, Mar. 2001. 2864 [AWD1] D. Awduche and Y. Rekhter, "Multiprocotol Lambda Switching: 2865 Combining MPLS Traffic Engineering Control with Optical 2866 Crossconnects", IEEE Communications Magazine, March 2001. 2868 [AWD2] D. Awduche, "MPLS and Traffic Engineering in IP Networks," 2869 IEEE Communications Magazine, Dec. 1999. 2871 [AWD3] D. Awduche, L. Berger, D. Gan, T. Li, G. Swallow, and V. 2872 Srinivasan, "RSVP-TE: Extensions to RSVP for LSP Tunnels," Work in 2873 Progress, Feb. 2001. 2875 [AWD4] D. Awduche, A. Hannan, X. Xiao, " Applicability Statement for 2876 Extensions to RSVP for LSP-Tunnels," Work in Progress, Apr. 2000. 2878 [AWD5] D. Awduche et al, "An Approach to Optimal Peering Between 2879 Autonomous Systems in the Internet," International Conference on 2880 Computer Communications and Networks (ICCCN'98), Oct. 1998. 2882 [CRUZ] R. L. Cruz, "A Calculus for Network Delay, Part II: Network 2883 Analysis," IEEE Transactions on Information Theory, vol. 37, pp. 2884 132-141, 1991. 2886 [DIFF-TE] F. Le Faucheur, et al, "Requirements for support of Diff- 2887 Serv-aware MPLS Traffic Engineering", Work in Progress, May 2001. 2889 [ELW95] A. Elwalid, D. Mitra and R.H. Wentworth, "A New Approach for 2890 Allocating Buffers and Bandwidth to Heterogeneous, Regulated Traffic 2891 in an ATM Node," IEEE IEEE Journal on Selected Areas in 2892 Communications, 13:6, pp. 1115-1127, Aug. 1995. 2894 [FGLR] A. Feldmann, A. Greenberg, C. Lund, N. Reingold, and J. 2896 Rexford, "NetScope: Traffic Engineering for IP Networks," IEEE 2897 Network Magazine, 2000. 2899 [FLJA93] S. Floyd and V. Jacobson, "Random Early Detection Gateways 2900 for Congestion Avoidance," IEEE/ACM Transactions on Networking, Vol. 2901 1 Nov. 4., p. 387-413, Aug. 1993. 2903 [FLOY94] S. Floyd, "TCP and Explicit Congestion Notification," ACM 2904 Computer Communication Review, V. 24, No. 5, p. 10-23, Oct. 1994. 2906 [HUSS87] B.R. Hurley, C.J.R. Seidl and W.F. Sewel, "A Survey of 2907 Dynamic Routing Methods for Circuit-Switched Traffic," IEEE 2908 Communication Magazine, Sep. 1987. 2910 [ITU-E600] ITU-T Recommendation E.600, "Terms and Definitions of 2911 Traffic Engineering," Mar. 1993. 2913 [ITU-E701] ITU-T Recommendation E.701, "Reference Connections for 2914 Traffic Engineering," Oct. 1993. 2916 [ITU-E801] ITU-T Recommendation E.801, "Framework for Service Quality 2917 Agreement," Oct. 1996. 2919 [JAM] B. Jamoussi, "Constraint-Based LSP Setup using LDP," Work in 2920 Progress, Feb. 2001. 2922 [KATZ] D. Katz, D. Yeung, and K. Kompella, "Traffic Engineering 2923 Extensions to OSPF," Work in Progress, Feb. 2001. 2925 [LNO96] T. Lakshman, A. Neidhardt, and T. Ott, "The Drop from Front 2926 Strategy in TCP over ATM and its Interworking with other Control 2927 Features," Proc. INFOCOM'96, p. 1242-1250, 1996. 2929 [MA] Q. Ma, "Quality of Service Routing in Integrated Services 2930 Networks," PhD Dissertation, CMU-CS-98-138, CMU, 1998. 2932 [MATE] A. Elwalid, C. Jin, S. Low, and I. Widjaja, "MATE: MPLS 2933 Adaptive Traffic Engineering," Proc. INFOCOM'01, Apr. 2001. 2935 [MCQ80] J.M. McQuillan, I. Richer, and E.C. Rosen, "The New Routing 2936 Algorithm for the ARPANET," IEEE. Trans. on Communications, vol. 28, 2937 no. 5, pp. 711-719, May 1980. 2939 [MPLS-DIFF] F. Le Faucheur, et al, "MPLS Support of Differentiated 2940 Services", Work in Progress, February 2001. 2942 [MR99] D. Mitra and K.G. Ramakrishnan, "A Case Study of Multiservice, 2943 Multipriority Traffic Engineering Design for Data Networks," Proc. 2944 Globecom'99, Dec 1999. 2946 [RFC-1349] P. Almquist, "Type of Service in the Internet Protocol 2947 Suite," RFC 1349, Jul. 1992. 2949 [RFC-1458] R. Braudes, S. Zabele, "Requirements for Multicast 2950 Protocols," RFC 1458, May 1993. 2952 [RFC-1771] Y. Rekhter and T. Li, "A Border Gateway Protocol 4 (BGP- 2953 4)," RFC 1771, Mar. 1995. 2955 [RFC-1812] F. Baker (Editor), "Requirements for IP Version 4 2956 Routers," RFC 1812, Jun. 1995. 2958 [RFC-1992] I. Castineyra, N. Chiappa, and M. Steenstrup, "The Nimrod 2959 Routing Architecture," RFC 1992, Aug. 1996. 2961 [RFC-1997] R. Chandra, P. Traina, and T. Li, "BGP Community 2962 Attributes" RFC 1997, Aug. 1996. 2964 [RFC-1998] E. Chen and T. Bates, "An Application of the BGP Community 2965 Attribute in Multi-home Routing," RFC 1998, Aug. 1996. 2967 [RFC-2178] J. Moy, "OSPF Version 2," RFC 2178, July 1997. 2969 [RFC-2205] R. Braden, et. al., "Resource Reservation Protocol (RSVP) 2970 - Version 1 Functional Specification," RFC 2205, Sep. 1997. 2972 [RFC-2211] J. Wroclawski, "Specification of the Controlled-Load 2973 Network Element Service," RFC 2211, Sep. 1997. 2975 [RFC-2212] S. Shenker, C. Partridge, R. Guerin, "Specification of 2976 Guaranteed Quality of Service," RFC 2212, Sep. 1997 2978 [RFC-2215] S. Shenker and J. Wroclawski, "General Characterization 2979 Parameters for Integrated Service Network Elements," RFC 2215, Sep. 2980 1997. 2982 [RFC-2216] S. Shenker and J. Wroclawski, "Network Element Service 2983 Specification Template," RFC 2216, Sep. 1997. 2985 [RFC-2330] V. Paxson et al., "Framework for IP Performance Metrics," 2986 RFC 2330, May 1998. 2988 [RFC-2386] E. Crawley, R. Nair, B. Rajagopalan, and H. Sandick, "A 2989 Framework for QoS-based Routing in the Internet," RFC 2386, Aug. 2990 1998. 2992 [RFC-2475] S. Blake et al., "An Architecture for Differentiated 2993 Services," RFC 2475, Dec. 1998. 2995 [RFC-2597] J. Heinanen, F. Baker, W. Weiss, and J. Wroclawski, 2996 "Assured Forwarding PHB Group," RFC 2597, June 1999. 2998 [RFC-2678] J. Mahdavi and V. Paxson, "IPPM Metrics for Measuring 2999 Connectivity," RFC 2678, Sep. 1999. 3001 [RFC-2679] G. Almes, S. Kalidindi, and M. Zekauskas, "A One-way Delay 3002 Metric for IPPM," RFC 2679, Sep. 1999. 3004 [RFC-2680] G. Almes, S. Kalidindi, and M. Zekauskas, "A One-way 3005 Packet Loss Metric for IPPM," RFC 2680, Sep. 1999. 3007 [RFC-2702] D. Awduche, J. Malcolm, J. Agogbua, M. O'Dell, J. McManus, 3008 "Requirements for Traffic Engineering over MPLS," RFC 2702, Sep. 3009 1999. 3011 [RFC-2722] N. Brownlee, C. Mills, and G. Ruth, "Traffic Flow 3012 Measurement: Architecture," RFC 2722, Oct. 1999. 3014 [RFC-2753] R. Yavatkar, D. Pendarakis, and R. Guerin, "A Framework 3015 for Policy-based Admission Control," RFC 2753, Jan. 2000. 3017 [RFC-2961] L. Berger, D. Gan, G. Swallow, P. Pan, F. Tommasi, S. 3018 Molendini, "RSVP Refresh Overhead Reduction Extensions", RFC 2961, 3019 Apr. 2000. 3021 [RFC-2998] Y. Bernet, et. al., "A Framework for Integrated Services 3022 Operation over Diffserv Networks", RFC 2998, Nov. 2000. 3024 [RFC-3031] E. Rosen, A. Viswanathan, R. Callon, "Multiprotocol Label 3025 Switching Architecture," RFC 3031, Jan. 2001. 3027 [SHAR] V. Sharma, et. al., "Framework for MPLS Based Recovery," Work 3028 in Progress, Mar. 2001. 3030 [SLDC98] B. Suter, T. Lakshman, D. Stiliadis, and A. Choudhury, 3031 "Design Considerations for Supporting TCP with Per-flow Queueing," 3032 Proc. INFOCOM'98, p. 299-306, 1998. 3034 [SMIT] H. Smit and T. Li, "IS-IS extensions for Traffic Engineering," 3035 Work in Progress, Feb. 2001. 3037 [XIAO] X. Xiao, A. Hannan, B. Bailey, L. Ni, "Traffic Engineering 3038 with MPLS in the Internet," IEEE Network magazine, Mar. 2000. 3040 [YARE95] C. Yang and A. Reddy, "A Taxonomy for Congestion Control 3041 Algorithms in Packet Switching Networks", IEEE Network Magazine, p. 3042 34-45, 1995. 3044 13.0 Authors' Addresses: 3046 Daniel O. Awduche 3047 Movaz Networks 3048 7926 Jones Branch Drive, Suite 615 3049 McLean, VA 22102 3050 Phone: 703-847-7350 3051 Email: awduche@movaz.com 3053 Angela Chiu 3054 Celion Networks 3055 1 Shiela Dr., Suite 2 3056 Tinton Falls, NJ 07724 3057 Phone: 732-747-9987 3058 Email: angela.chiu@celion.com 3060 Anwar Elwalid 3061 Lucent Technologies 3062 Murray Hill, NJ 07974 3063 Phone: 908 582-7589 3064 Email: anwar@lucent.com 3066 Indra Widjaja 3067 Bell Labs, Lucent Technologies 3068 600 Mountain Avenue 3069 Murray Hill, NJ 07974 3070 Phone: 908 582-0435 3071 Email: iwidjaja@research.bell-labs.com 3073 XiPeng Xiao 3074 Photuris Inc. 3075 2025 Stierlin Ct., 3076 Mountain View, CA 94043 3077 Phone: 650-919-3215 3078 Email: xxiao@photuris.com