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Run idnits with the --verbose option for more detailed information about the items above. -------------------------------------------------------------------------------- 1 TEAS Working Group A.Wang 2 Internet Draft China Telecom 3 Xiaohong Huang 4 BUPT 5 Caixia Qou 6 BUPT 7 Lu Huang 8 China Mobile 9 Penghui Mi 10 Tencent Company 12 Intended status: Information Track June 30, 2017 13 Expires: December 29, 2018 15 CCDR Scenario, Simulation and Suggestion 16 draft-wang-teas-ccdr-00.txt 18 Status of this Memo 20 This Internet-Draft is submitted in full conformance with the 21 provisions of BCP 78 and BCP 79. 23 This Internet-Draft is submitted in full conformance with the 24 provisions of BCP 78 and BCP 79. This document may not be modified, 25 and derivative works of it may not be created, and it may not be 26 published except as an Internet-Draft. 28 This Internet-Draft is submitted in full conformance with the 29 provisions of BCP 78 and BCP 79. This document may not be modified, 30 and derivative works of it may not be created, except to publish it 31 as an RFC and to translate it into languages other than English. 33 it for publication as an RFC or to translate it into languages other 34 than English. 36 Internet-Drafts are working documents of the Internet Engineering 37 Task Force (IETF), its areas, and its working groups. Note that 38 other groups may also distribute working documents as Internet- 39 Drafts. 41 Internet-Drafts are draft documents valid for a maximum of six 42 months and may be updated, replaced, or obsoleted by other documents 43 at any time. It is inappropriate to use Internet-Drafts as 44 reference material or to cite them other than as "work in progress." 46 The list of current Internet-Drafts can be accessed at 47 http://www.ietf.org/ietf/1id-abstracts.txt 48 The list of Internet-Draft Shadow Directories can be accessed at 49 http://www.ietf.org/shadow.html 51 This Internet-Draft will expire on December 30, 2017. 53 Copyright Notice 55 Copyright (c) 2017 IETF Trust and the persons identified as the 56 document authors. All rights reserved. 58 This document is subject to BCP 78 and the IETF Trust's Legal 59 Provisions Relating to IETF Documents 60 (http://trustee.ietf.org/license-info) in effect on the date of 61 publication of this document. Please review these documents 62 carefully, as they describe your rights and restrictions with 63 respect to this document. 65 Abstract 67 This document describes the scenarios, simulation and suggestions 68 for the "Centrally Control Dynamic Routing (CCDR)" architecture, 69 which integrates the merit of traditional distributed protocols 70 (IGP/BGP), and the power of centrally control technologies (PCE/SDN) 71 to provide one feasible traffic engineering solution in various 72 complex scenarios for the service provider. 74 Traditional MPLS-TE solution is mainly used in static network 75 planning scenario and is difficult to meet the QoS assurance 76 requirements in real-time traffic network. With the emerge of SDN 77 concept and related technologies, it is possible to simplify the 78 complexity of distributed control protocol, utilize the global view 79 of network condition, give more efficient solution for traffic 80 engineering in various complex scenarios. 82 Table of Contents 84 1. Introduction ................................................ 3 85 2. Conventions used in this document............................ 4 86 3. CCDR Scenarios. ............................................. 4 87 3.1. Qos Assurance for Hybrid Cloud-based Application........ 4 88 3.2. Increase link utilization based on tidal phenomena...... 5 89 3.3. Traffic engineering for IDC/MAN asymmetric link..........6 90 3.4. Network temporal congestion elimination................. 6 91 4. CCDR Simulation. ............................................ 7 92 4.1. Topology Simulation..................................... 7 93 4.2. Traffic Matrix Simulation............................... 8 94 4.3. End-to-End Path Optimization............................ 8 95 4.4. Network temporal congestion elimination .................9 97 5. CCDR Deployment Consideration............................... 11 98 6. Security Considerations..................................... 11 99 7. IANA Considerations ........................................ 11 100 8. Conclusions ................................................ 11 101 9. References ................................................. 11 102 9.1. Normative References................................... 11 103 9.2. Informative References................................. 12 104 10. Contributors: ............................................. 13 105 11. Acknowledgments ........................................... 13 107 1. Introduction 109 Internet network is composed mainly tens of thousands of routers that 110 run distributed protocol to exchange the reachability information 111 between them. The path for the destination network is mainly 112 calculated and controlled by the traditional IGP protocol. These 113 distributed protocols are robust enough to support the current 114 evolution of Internet but has some difficulties when the application 115 requires the end-to-end QoS performance, or the service provider 116 wants to maximize the links utilization within their network. 118 MPLS-TE technology is one perfect solution for the finely planned 119 network but it will put heavy burden on the router when we use it to 120 solve the dynamic QoS assurance requirements within real time traffic 121 network. 123 SR(Segment Routing) is another prominent solution that integrates 124 some merits of traditional distributed protocol and the advantages of 125 centrally control mode, but it requires the underlying network, 126 especially the provider edge router to do label push and pop action 127 in-depth, and need some complex solutions for co-exist with the Non- 128 SR network. Finally, it can only maneuver the end-to-end path for 129 MPLS and IPv6 traffic via different mechanism. 131 The advantage of MPLS is mainly for traffic isolation, such as the 132 L2/L3 VPN service deployments. With the emerge of cloud-based 133 services, especially the hybrid cloud communication services, the 134 customers requires mainly the end-to-end QoS assurance services 135 between their private infrastructure and the rented public servers. 136 Without the help of centrally control architecture, the service 137 provider almost can't make such SLA guarantees upon the real time 138 traffic situation. 140 This draft gives some scenarios that the centrally control dynamic 141 routing (CCDR) architecture can easily solve, without adding more 142 extra burdening on the router. It also gives the PCE algorithm 143 results under the similar topology, traffic pattern and network size 144 to illustrate the applicability of CCDR architecture. Finally, it 145 gives some suggestions for the implementation and deployment of CCDR. 147 2. Conventions used in this document 149 The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", 150 "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this 151 document are to be interpreted as described in RFC 2119 [RFC2119]. 153 3. CCDR Scenarios. 155 The following sections describe some scenarios that the CCDR 156 architecture is suitable for deployment. 158 3.1. Qos Assurance for Hybrid Cloud-based Application. 160 With the emerge of cloud computing technologies, enterprises are 161 putting more and more services on the public oriented service 162 infrastructure, but keep still some core services within their 163 network. The bandwidth requirements between the private cloud and 164 the public cloud are occasionally and the background traffic between 165 these two sites varied from time to time. Enterprise cloud 166 applications just want to have the capabilities to invoke the 167 network to make the end-to-end QoS assurance on demand. Otherwise, 168 the traffic should be controlled by the default distributed protocol. 170 CCDR, which integrates the merits of distributed protocol and the 171 power of centrally control, is suitable for this scenario. The 172 possible solution architecture is illustrated below: 174 +------------------------+ 175 | Cloud Based Application| 176 +------------------------+ 177 | 178 +-----------+ 179 | PCE | 180 +-----------+ 181 | 182 | 183 //--------------\\ 184 ///// \\\\\ 185 Private Cloud Site || Distributed |Public Cloud Site 186 | Control Network | 187 \\\\\ ///// 188 \\--------------// 190 Fig.1 Hybrid Cloud Communication Scenario 192 By default, the traffic path between the private cloud site and 193 public cloud site will be determined by the distributed control 194 network. When some applications require the end-to-end QoS assurance, 195 it can send these requirements to PCE, let PCE compute one e2e path 196 which is based on the underlying network topology and the real 197 traffic information, to accommodate the application's bandwidth 198 requirements. The proposed solution can refer the draft [draft-wang- 199 teas-pce-native-ip]. Section 4 describes the detail simulation 200 process and the results. 202 3.2. Increase link utilization based on tidal phenomena. 204 Currently, the network topology within MAN is generally in star 205 style as illustrated in Fig.2, with the different devices connect 206 different kind customer. The traffic pattern of these customers 207 demonstrates some tidal phenomena that the links between the CR/BRAS 208 and CR/SR will experience congestion in different periods because 209 the subscribers under BRAS often use the network at night and the 210 dedicated line users under SR often use the network during the 211 daytime. The uplink between BRAS/SR and CR must satisfy the maximum 212 traffic pattern between them and this causes the links utilization 213 always not efficient enough. 215 +--------+ 216 | CR | 217 +----|---+ 218 | 219 --------|--------|-------| 220 | | | | 221 +--|-+ +-|- +--|-+ +-|+ 222 |BRAS| |SR| |BRAS| |SR| 223 +----+ +--+ +----+ +--+ 225 Fig.2 STAR-style network topology within MAN 227 If we can consider link the BRAS/SR with local loop, and control the 228 MAN with the CCDR architecture, we can exploit the tidal phenomena 229 between BRAS/CR and SR/CR links, increase the efficiency of them. 231 +-------+ 232 ----- PCE | 233 | +-------+ 234 +----|---+ 235 | CR | 236 +----|---+ 237 | 238 --------|--------|-------| 239 | | | | 240 +--|-+ +-|- +--|-+ +-|+ 241 |BRAS-----SR| |BRAS-----SR| 242 +----+ +--+ +----+ +--+ 244 Fig.3 Increase the link utilization via CCDR 246 3.3. Traffic engineering for IDC/MAN asymmetric link 248 The operator's networks are often comprised by tens of different 249 domains, interconnected with each other, form very complex topology 250 that illustrated in Fig.4. Due to the traffic pattern to/from MAN 251 and IDC, the links between them are often in asymmetric style. It is 252 almost impossible to balance the utilization of these links via the 253 traditional distributed protocol, but this unbalance phenomenon can 254 be overcome via the CCDR architecture. 256 +---+ +---+ 257 |MAN|-----------------IDC| 258 +-|-| | +-|-+ 259 | ---------| | 260 ------|BackBone|------ 261 | ----|----| | 262 | | | 263 +-|-- | ----+ 264 |IDC|----------------|MAN| 265 +---| |---+ 267 Fig.4 TE within Complex Multi-Domain topology 269 3.4. Network temporal congestion elimination. 271 In more general situation, there are often temporal congestion 272 periods within part of the service provider's network. Such 273 congestion phenomena will appear repeatedly and if the service 274 provider has some methods to mitigate it, it will certainly increase 275 the satisfaction degree of their customer. CCDR is also suitable for 276 such scenario that the traditional distributed protocol will process 277 most of the traffic forwarding and the controller will schedule some 278 traffic out of the congestion links to lower the utilization of them. 279 Section 4 describes the simulation process and results about such 280 scenario. 282 4. CCDR Simulation. 284 The following sections describe the topology, traffic matrix, end- 285 to-end path optimization and congestion elimination in CCDR 286 simulation. 288 4.1. Topology Simulation. 290 Technically, the topology involved nodes and links state information 291 is significantly helpful for traffic schedule. 293 The network topology mainly contains nodes and links information. 294 Nodes used in simulation have two types: core nodes and edge nodes. 295 The core nodes are fully linked to each other. The edge nodes are 296 connected with some of the core nodes. And edge nodes are not 297 connected with other edge nodes directly. Fig.5 is a topology 298 example of 4 core nodes and 5 edge nodes. In this simulation, 100 299 core nodes and 400 edge nodes are generated. 301 +----+ 302 /|Edge|\ 303 | +----+ | 304 | | 305 | | 306 +----+ +----+ +----+ 307 |Edge|----|Core|-----|Core|---------+ 308 +----+ +----+ +----+ | 309 / | \ / | | 310 +----+ | \ / | | 311 |Edge| | X | | 312 +----+ | / \ | | 313 \ | / \ | | 314 +----+ +----+ +----+ | 315 |Edge|----|Core|-----|Core| | 316 +----+ +----+ +----+ | 317 | | | 318 | +------\ +----+ 319 | ---|Edge| 320 +-----------------/ +----+ 322 Fig.5 Topology of simulation 324 The total number of links is set to be more than 20000. The number 325 of links connecting one edge node to the set of core nodes is 326 randomly between 2 to 30. The bandwidth of all links is set to be 327 100Gbps. The metric of links between core nodes themselves is set to 328 be from 60 to 100, while metric of links between core nodes and edge 329 nodes is set to be from 1000 to 1060. The metric of links is used 330 for selecting the shortest paths of all source-destination pairs. 331 Besides, each link has its congestion threshold. For the links 332 between core nodes, the threshold is set to be 0.8 which means when 333 its utilization is beyond 80% the link is overloaded. Otherwise, the 334 link is not congested. Similarly, the threshold of links between an 335 edge node and a core node is set to be 0.9. 337 4.2. Traffic Matrix Simulation. 339 The end-to-end traffic of the network is regard as a n*n matrix 340 where n stands for the number of forwarding devices in the network. 341 Each (i,j) component of traffic matrix denotes the bandwidth of the 342 flow from i-th node to j-th node. The traffic matrix is generated 343 based on the link capacity of topology. It can result in many kinds 344 of situations, such as congestion, mild congestion and non- 345 congestion. 347 In this simulation, the traffic matrix is 500*500. The components of 348 traffic matrix are generated from 10Mbps to 7Gbps randomly. About 20% 349 links are overloaded when the Open Shortest Path First (OSPF) 350 protocol is used in the network. 352 This traffic matrix is used in following sections. In section 4.3, 353 it is used as the background traffic which can't be scheduled. In 354 section 4.4, it is re-routed based on load-balance. 356 4.3. End-to-End Path Optimization 358 Based on the current state of the network, such as the traffic 359 matrix in the network, network topology and network utilization, 360 Quality of Service (QoS) and so on, the end-to-end path optimization 361 is to find the best end-to-end path which is the lowest in metric 362 value and each link of the path is far below link's threshold. The 363 algorithm is a novel idea combining the shortest path algorithm with 364 penalty theory of classical optimization and graph theory. 366 Given background traffic matrix which is unscheduled, when a set of 367 new flows comes into the network the end-to-end path optimization 368 finds the optimal paths for them. The selected paths bring the least 369 congestion degree to the network. 371 The simulation is tested with 1000 flows in 6 periods. The size of 372 flows is from 10Mbps to 10Gbps. In each period, 100, 200, 100, 250, 373 150 and 200 flows are arrived respectively. The link utilization 374 increment(UI) degree relative to the congestion threshold when the 375 new flows are added into the network is shown in Fig.6. The first 376 graph in Fig.6 is the UI with OSPF and the second graph is the UI 377 with end-to-end path optimization. The average UI of graph one is 378 more than 30%. After path optimization as shown in graph, the 379 average UI is less than 5%. In a conclude, the results show that the 380 end-to-end path optimization has an eye-catching decreasing in UI 381 degree relative to the path chosen based on OSPF. 383 +-----------------------------------------------------------+ 384 | * * * *| 385 60| * * * * * *| 386 |* * ** * * * * * ** * * * * **| 387 |* * ** * * ** *** ** * * ** * * * ** * * *** **| 388 |* * * ** * ** ** *** *** ** **** ** *** **** ** *** **| 389 40|* * * ***** ** *** *** *** ** **** ** *** ***** ****** **| 390 UI(%) |* * ******* ** *** *** ******* **** ** *** ***** *********| 391 |*** ******* ** **** *********** *********** ***************| 392 |******************* *********** *********** ***************| 393 20|******************* ***************************************| 394 |******************* ***************************************| 395 |***********************************************************| 396 |***********************************************************| 397 0+-----------------------------------------------------------+ 398 0 100 200 300 400 500 600 700 800 900 1000 399 +-----------------------------------------------------------+ 400 | | 401 60| | 402 | | 403 | | 404 | | 405 40| | 406 UI(%) | | 407 | | 408 | | 409 20| | 410 | *| 411 | * *| 412 | * * * * * ** * *| 413 0+-----------------------------------------------------------+ 414 0 100 200 300 400 500 600 700 800 900 1000 415 Flow Number 416 Fig.6 Simulation result with congestion elimination 418 4.4. Network temporal congestion elimination 420 In general situation, there are often temporal congestion periods 421 within part of the service provider's network. The network temporal 422 congestion elimination is proposed which reroutes traffic from the 423 congested paths to un-congested ones. The load-balance is achieved 424 after congestion elimination. And the cost of reroute traffic is 425 also taken into consideration. 427 Different degree of network congestion is simulated. About 20% links 428 are congested with slightly or badly degree using the OSPF protocol. 429 The congestion degree (CD) is defined as the link utilization beyond 430 its threshold. For example, if the utilization of links is 90%, and 431 its threshold is 80%, then its CD is 10%. 433 The congestion elimination performance is shown in Fig.7. The first 434 graph is the congestion degree before the process of congestion 435 elimination. The average CD of all congested links is more than 10%. 436 The second graph shown in Fig.7 is the congestion degree after 437 congestion elimination process. It shows only 12 links among totally 438 2000 links exceed the threshold, and all the congestion degree is 439 less than 3%. Thus, after schedule of the traffic in congestion 440 paths, the degree of network congestion is greatly eliminated and 441 the network utilization is indeed in balance. 443 Before congestion elimination 444 +-----------------------------------------------------------+ 445 | * ** * ** ** *| 446 20| * * **** * ** ** *| 447 |* * ** * ** ** **** * ***** *********| 448 |* * * * * **** ****** * ** *** **********************| 449 15|* * * ** * ** **** ********* *****************************| 450 |* * ****** ******* ********* *****************************| 451 CD(%) |* ********* ******* ***************************************| 452 10|* ********* ***********************************************| 453 |*********** ***********************************************| 454 |***********************************************************| 455 5|***********************************************************| 456 |***********************************************************| 457 |***********************************************************| 458 0+-----------------------------------------------------------+ 459 0 0.5 1 1.5 2 461 After congestion elimination 462 +-----------------------------------------------------------+ 463 | | 464 20| | 465 | | 466 | | 467 15| | 468 | | 469 CD(%) | | 470 10| | 471 | | 472 | | 474 5 | | 475 | | 476 | * ** * * * ** * ** * | 477 0 +-----------------------------------------------------------+ 478 0 0.5 1 1.5 2 479 Link Number(*10000) 480 Fig.7 Simulation result with congestion elimination 482 5. CCDR Deployment Consideration. 484 With the above scenarios and simulation results, we can know it is 485 necessary to find one general solution to cope with various complex 486 situations and it is possible to accomplish the most complex optimal 487 path computation function in centrally manner based on the underlay 488 network topology and the real time traffic. 490 [draft-wang-teas-native-ip] gives one basic solution for above 491 scenario, such thought can be extended to cover requirements that 492 are more concretes. 494 6. Security Considerations 496 TBD 498 7. IANA Considerations 500 TBD 502 8. Conclusions 504 TBD 506 9. References 508 9.1. Normative References 510 [RFC4655] Farrel, A., Vasseur, J.-P., and J. Ash, "A Path 512 Computation Element (PCE)-Based Architecture", RFC 514 4655, August 2006,. 516 [RFC5440]Vasseur, JP., Ed., and JL. Le Roux, Ed., "Path 518 Computation Element (PCE) Communication Protocol 520 (PCEP)", RFC 5440, March 2009, 521 . 523 9.2. Informative References 525 [I-D.draft-ietf-teas-pce-control-function] 527 A.Farrel, Q.Zhao et al. "An Architecture for use of PCE and PCEP in 528 a Network with Central Control" 530 https://datatracker.ietf.org/doc/draft-ietf-teas-pce-central- 531 control/ September, 2016 533 [I-D. draft-ietf-teas-pcecc-use-cases] 535 Quintin Zhao, Robin Li, Boris Khasanov et al. "The Use Cases for 536 Using PCE as the Central Controller(PCECC) of LSPs 538 https://tools.ietf.org/html/draft-ietf-teas-pcecc-use-cases-00 540 March,2017 542 [I-D. draft-wang-teas-pce-native-ip] 544 A.Wang, Quintin Zhao, Boris Khasanov, Penghui Mi,Raghavendra Mallya, 545 Shaofu Peng "PCE in Native IP Network" 547 https://tools.ietf.org/html/draft-wang-teas-pce-native-ip-03 March 548 13, 2017 550 [I-D. draft-wang-pcep-extension for native IP] 552 Aijun Wang, Boris Khasanov et al. "PCEP Extension for Native IP 553 Network" https://datatracker.ietf.org/doc/draft-wang-pce-extension- 554 native-ip/ 556 10. Contributors: 558 Tingting Yuan 559 Beijing University of Posts and Telecommunications 560 yuantingting@bupt.edu.cn 562 Dingyuan Hu 563 Beijing University of Posts and Telecommunications 564 hdy@bupt.edu.cn 566 11. Acknowledgments 568 TBD 570 Authors' Addresses 572 Aijun Wang 573 China Telecom 574 Beiqijia Town, Changping District 575 Beijing,China 577 Email: wangaj.bri@chinatelecom.cn 579 Xiaohong Huang 580 Beijing University of Posts and Telecommunications 581 No.10 Xitucheng Road, Haidian District 582 Beijing,China 584 EMail: huangxh@bupt.edu.cn 586 Caixia Qou 587 Beijing University of Posts and Telecommunications 588 No.10 Xitucheng Road, Haidian District 589 Beijing,China 590 koucx@lsec.cc.ac.cn 591 Lu Huang 592 China Mobile 593 32 Xuanwumen West Ave, Xicheng District 594 Beijing 100053 595 China 596 Email: hlisname@yahoo.com 598 Penghui Mi 599 Tencent 600 Tencent Building, Kejizhongyi Avenue, 601 Hi-techPark, Nanshan District,Shenzhen 518057, P.R.China 603 Email kevinmi@tencent.com