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Checking references for intended status: Informational ---------------------------------------------------------------------------- No issues found here. Summary: 0 errors (**), 0 flaws (~~), 1 warning (==), 1 comment (--). Run idnits with the --verbose option for more detailed information about the items above. -------------------------------------------------------------------------------- 2 Network Working Group I. Rimac 3 Internet-Draft V. Hilt 4 Intended status: Informational M. Tomsu 5 Expires: December 30, 2010 V. Gurbani 6 Bell Labs, Alcatel-Lucent 7 E. Marocco 8 Telecom Italia 9 June 28, 2010 11 A Survey on Research on the Application-Layer Traffic Optimization 12 (ALTO) Problem 13 draft-irtf-p2prg-alto-survey-05 15 Abstract 17 A significant part of the Internet traffic today is generated by 18 Peer-to-peer (P2P) applications used traditionally for file-sharing, 19 and more recently for real-time communications and live media 20 streaming. Such applications discover a route to each other through 21 an overlay network with little knowledge of the underlying network 22 topology. As a result, they may choose peers based on information 23 deduced from empirical measurements, which can lead to suboptimal 24 choices. This document, a product of the P2P Research Group, 25 presents a survey of existing literature on discovering and using 26 network topology information for application-layer traffic 27 optimization. 29 Status of this Memo 31 This Internet-Draft is submitted in full conformance with the 32 provisions of BCP 78 and BCP 79. 34 Internet-Drafts are working documents of the Internet Engineering 35 Task Force (IETF). Note that other groups may also distribute 36 working documents as Internet-Drafts. The list of current Internet- 37 Drafts is at http://datatracker.ietf.org/drafts/current/. 39 Internet-Drafts are draft documents valid for a maximum of six months 40 and may be updated, replaced, or obsoleted by other documents at any 41 time. It is inappropriate to use Internet-Drafts as reference 42 material or to cite them other than as "work in progress." 44 This Internet-Draft will expire on December 30, 2010. 46 Copyright Notice 48 Copyright (c) 2010 IETF Trust and the persons identified as the 49 document authors. All rights reserved. 51 This document is subject to BCP 78 and the IETF Trust's Legal 52 Provisions Relating to IETF Documents 53 (http://trustee.ietf.org/license-info) in effect on the date of 54 publication of this document. Please review these documents 55 carefully, as they describe your rights and restrictions with respect 56 to this document. Code Components extracted from this document must 57 include Simplified BSD License text as described in Section 4.e of 58 the Trust Legal Provisions and are provided without warranty as 59 described in the Simplified BSD License. 61 Table of Contents 63 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 3 64 1.1. Terminology . . . . . . . . . . . . . . . . . . . . . . . 4 65 2. Survey of Existing Literature . . . . . . . . . . . . . . . . 4 66 2.1. Application-Level Topology Estimation . . . . . . . . . . 5 67 2.2. Topology Estimation through Layer Cooperation . . . . . . 8 68 2.2.1. P4P Architecture . . . . . . . . . . . . . . . . . . . 9 69 2.2.2. Oracle-based ISP-P2P Collaboration . . . . . . . . . . 9 70 2.2.3. ISP-Driven Informed Path Selection (IDIPS) Service . . 10 71 3. Application-Level Topology Estimation and the ALTO Problem . . 10 72 4. Open Issues . . . . . . . . . . . . . . . . . . . . . . . . . 12 73 4.1. Coordinate estimation or path latencies? . . . . . . . . . 12 74 4.2. Malicious nodes . . . . . . . . . . . . . . . . . . . . . 12 75 4.3. Information integrity . . . . . . . . . . . . . . . . . . 12 76 4.4. Richness of topological information . . . . . . . . . . . 13 77 4.5. Hybrid solutions . . . . . . . . . . . . . . . . . . . . . 13 78 4.6. Negative impact of over-localization . . . . . . . . . . . 13 79 5. Security Considerations . . . . . . . . . . . . . . . . . . . 14 80 6. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 14 81 7. Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . 14 82 8. Informative References . . . . . . . . . . . . . . . . . . . . 14 83 Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 18 85 1. Introduction 87 A significant part of today's Internet traffic is generated by Peer- 88 to-peer (P2P) applications, used originally for file sharing, and 89 more recently for realtime multimedia communications and live media 90 streaming. P2P applications are posing serious challenges to the 91 Internet infrastructure; by some estimates, P2P systems are so 92 popular that they make up anywhere between 40% to 85% of the entire 93 Internet traffic [Meeker], [Karagiannis], [LightReading], 94 [LinuxReviews], [Parker], [Glasner]. 96 P2P systems ensure that popular content is replicated at multiple 97 instances in the overlay. But perhaps ironically, a peer searching 98 for that content may ignore the topology of the latent overlay 99 network and instead select among available instances based on 100 information it deduces from empirical measurements, which, in some 101 particular situations may lead to suboptimal choices. For example, a 102 shorter round-trip time estimation is not indicative of the bandwidth 103 and reliability of the underlying links, which have more of an 104 influence than delay for large file transfer P2P applications. 106 Most Distributed Hash Tables (DHT) -- the data structure that imposes 107 a specific ordering for P2P overlays -- use greedy forwarding 108 algorithms to reach their destination, making locally optimal 109 decisions that may not turn out to be globally optimized [Gummadi]. 110 This naturally leads to the Application-Layer Traffic Optimization 111 (ALTO) problem [RFC5693]: how to best provide the topology of the 112 underlying network while at the same time allowing the requesting 113 node to use such information to effectively reach the node on which 114 the content resides. Thus, it would appear that P2P networks with 115 their application layer routing strategies based on overlay 116 topologies are in direct competition against the Internet routing and 117 topology. 119 One way to solve the ALTO problem is to build distributed 120 application-level services for location and path selection [Francis], 121 [Ng], [Dabek], [Costa], [Wong], [Madhyastha], in order to enable 122 peers to estimate their position in the network and to efficiently 123 select their neighbors. Similar solutions have been embedded into 124 P2P applications such as Azureus [Azureus]. A slightly different 125 approach is to have the Internet Service Provider (ISP) take a pro- 126 active role in the routing of P2P application traffic; the means by 127 which this can be achieved have been proposed [Aggarwal], [Xie], 128 [Saucez]. There is an intrinsic struggle between the layers -- P2P 129 overlay and network underlay -- when performing the same service 130 (routing), however there are strategies to mitigate this dichotomy 131 [Seetharaman]. 133 This document, initially intended as a complement to RFC 5693 134 [RFC5693] and discussed during the creation of the IETF ALTO Working 135 Group, has been completed and refined in the IRTF P2P Research Group. 136 Its goal is to summarize the contemporary research activities on the 137 application layer traffic optimization problem as input to the ALTO 138 working group protocol designers. 140 1.1. Terminology 142 Terminology adopted in this document includes terms such as "ring 143 geometry", "tree structure", "butterfly network" borrowed from P2P 144 scientific literature. [RFC4981] provides an exaustive definition of 145 such terminology. 147 Certain security-related terms are to be understood in the sense 148 defined in [RFC4949]; such terms include, but are not limited to, 149 "attack", "authentication", "confidentiality", "encryption", 150 "identity", "integrity". Other security-related terms (for example, 151 "denial of service") are to be understood in the sense defined in the 152 referenced specifications. 154 2. Survey of Existing Literature 156 Gummadi et al. [Gummadi] compare popular DHT algorithms and besides 157 analyzing their resilience, provide an accurate evaluation of how 158 well the logical overlay topology maps on the physical network layer. 159 In their paper, relying only on measurements independently performed 160 by overlay nodes without the support of additional location 161 information provided by external entities, they demonstrate that the 162 most efficient algorithms in terms of resilience and proximity 163 performance are those based on the simplest geometric concept (i.e. 164 the ring geometry, rather than tree structures, butterfly networks 165 and hybrid geometries). 167 Regardless of the geometrical properties of the distributed data 168 structures involved, interactions between application-layer overlays 169 and the underlying networks are a rich area of investigation. The 170 available literature in this field can be divided in two categories 171 (Figure 1): using application-level techniques to estimate topology 172 and through some kind of layer cooperation. 174 Application-layer traffic optimization 175 | 176 +--> Application-level topology estimation 177 | | 178 | +--> Coordinates-based systems 179 | | | 180 | | +--> GNP 181 | | | 182 | | +--> Vivaldi 183 | | | 184 | | +--> PIC 185 | | 186 | +--> Path selection services 187 | | | 188 | | +--> IDMaps 189 | | | 190 | | +--> Meridian 191 | | | 192 | | +--> Ono 193 | | 194 | +--> Link-layer Internet maps 195 | | 196 | +--> iPlane 197 | 198 +--> Topology estimation through layer cooperation 199 | 200 +--> P4P: Provider portal for applications 201 | 202 +--> Oracle-based ISPs and P2P cooperation 203 | 204 +--> ISP-driven informed path selection 206 Taxonomy of solutions for the application-layer traffic optimization 207 problem. 209 Figure 1 211 2.1. Application-Level Topology Estimation 213 Estimating network topology information on the application layer has 214 been an area of active research. Early systems used triangulation 215 techniques to bound the distance between two hosts using a common 216 landmark host. In such a technique, given a cost function C, a set 217 of vertexes V and their corresponding edges, the triangle inequality 218 holds if for any triple {a, b, c} in V, C(a, c) is always less than 219 or equal to C(a, g) + C(b, c). The cost function C could be 220 expressed in terms of desirable metrics such as bandwidth or latency. 222 We note that the techniques presented in this section are only 223 representative of the sizable research in this area. Rather than 224 trying to enumerate an exhaustive list, we have chosen certain 225 techniques because they represent an advance in the area that further 226 led to derivative works. 228 Francis et al. proposed IDMaps [Francis], a system where one or more 229 special hosts called tracers are deployed near an autonomous system. 230 The distance measured in Round Trip Time (RTT) between hosts A and B 231 is estimated as the cumulative distance between A and its nearest 232 tracer Ta, plus the distance between B and its nearest tracer Tb, 233 plus the shortest distance from Ta to Tb. To aid in scalability 234 beyond that provided by the client-server design of IDMaps, Ng et al. 235 proposed a P2P-based Global Network Positioning (GNP) architecture 236 [Ng]. GNP was a network coordinate system based on absolute 237 coordinates computed from modeling the Internet as a geometric space. 238 It proposed a two-part architecture: in the first part, a small set 239 of finite distributed hosts called landmarks compute their own 240 coordinates in a fixed geometric space. In the second part, a host 241 wishing to participate computes its own coordinates relative to those 242 of the landmark hosts. Thus, armed with the computed coordinates, 243 hosts can then determine interhost distance as soon as they discover 244 each other. 246 Both IDMaps and GNP require fixed network infrastructure support in 247 the form of tracers or landmark hosts; this often introduces a single 248 point of failure and inhibits scalability. To combat this, new 249 techniques were developed that embedded the network topology in a 250 low-dimensional coordinate space to enable network distance 251 estimation through vector analysis. Costa et al. introduced 252 Practical Internet Coordinates (PIC) [Costa]. While PIC used the 253 notion of landmark hosts, it did not require explicit network support 254 to designate specific landmark hosts. Any node whose coordinates 255 have been computed could act as a landmark host. When a node joined 256 the system, it probed the network distance to some landmark hosts. 257 Then, it obtained the coordinates of each landmark host and computed 258 its own coordinates relative to the landmark host, subject to the 259 constraint of minimizing the error in the predicted distance and 260 computed distance. 262 Like PIC, Vivaldi [Dabek] proposed a fully distributed network 263 coordinate systems without any distinguished hosts. Whenever a node 264 A communicates with another node B, it measures the RTT to that node 265 and learns that node's current coordinates. A subsequently adjusts 266 its coordinates such that it is closer to, or further from B by 267 computing new coordinates that minimize the squared error. A Vivaldi 268 node is thus constantly adjusting it's position based on a simulation 269 of interconnected mass springs. Vivaldi is now being used in the 270 popular P2P application Azureus and studies indicate that it scales 271 well to very large networks [Ledlie]. 273 Network coordinate systems require the embedding of the Internet 274 topology into a coordinate system. This is not always possible 275 without errors, which impacts the accuracy of distance estimations. 276 In particular, it has proved to be difficult to embed the triangular 277 inequalities found in Internet path distances [Ledlie]. Thus, 278 Meridian [Wong] abandons the generality of network coordinate systems 279 and provides specific distance evaluation services. In Meridian, 280 each node keeps track of small fixed number of neighbors and 281 organizes them in concentric rings, ordered by distance from the 282 node. Meridian locates the closest node by performing a multi-hop 283 search where each hop exponentially reduces the distance to the 284 target. Although less general than virtual coordinates, Meridian 285 incurs significantly less error for closest node discovery. 287 The Ono project [Ono] takes a different approach and uses network 288 measurements from Content Distribution Network (CDN) like Akamai to 289 find nearby peers. Used as a plugin to the Azureus BitTorrent 290 client, Ono provides 31% average download rate improvement [Su]. 292 Comparison of application-level topology estimation techniques, as 293 reported in literature. Results in terms of number of (D)imensions 294 and (L)andmarks, 90th percentile relative error. 296 +----------------+---------------+----------------+-----------------+ 297 | GNP vs. | PIC(b) vs. | Vivaldi vs. | Meridian vs. | 298 | IDMaps(a) (7D, | GNP (8D, 16L) | GNP (2D, 32L) | GNP (8D, 15L) | 299 | 15L) | | | | 300 +----------------+---------------+----------------+-----------------+ 301 | GNP: 0.50, | PIC: 0.38, | Vivaldi: 0.65, | Meridian: 0.78, | 302 | IDMaps: 0.97 | GNP: 0.37 | GNP: 0.65 | GNP: 1.18 | 303 +----------------+---------------+----------------+-----------------+ 305 (a) Does not use dimensions or landmarks. (b) Using results from the 306 hybrid strategy for PIC. 308 Table 1 310 Table 1 summarizes the application-level topology estimation 311 techniques. The salient performance metric is the relative error. 312 While all approaches define this metric a bit differently, it can be 313 generalized as how close a predicted distance comes to the 314 corresponding measured distance. A value of zero implies perfect 315 prediction and a value of 1 implies that the predicted distance is in 316 error by a factor of two. PIC, Vivaldi, and Meridian compare their 317 results with that of GNP, while GNP itself compares its results with 318 a precursor technique, IDMaps. Because each of the techniques uses a 319 different Internet topology and a varying number of landmarks and 320 dimensions to interpret the data set, it is impossible to normalize 321 the relative error across all techniques uniformly. Thus we present 322 the relative error data in pairs, as reported in the literature 323 describing the specific technique. Readers are urged to compare the 324 relative error performance in each column on its own and not draw any 325 conclusions by comparing the data across columns. 327 Most of the work on estimating topology information focuses on 328 predicting network distance in terms of latency and does not provide 329 estimates for other metrics such as throughput or packet loss rate. 330 However, for many P2P applications latency is not the most important 331 performance metric and these applications could benefit from a richer 332 information plane. Sophisticated methods of active network probing 333 and passive traffic monitoring are generally very powerful and can 334 generate network statistics indirectly related to performance 335 measures of interest, such as delay and loss rate on link-level 336 granularity. Extraction of these hidden attributes can be achieved 337 by applying statistical inference techniques developed in the field 338 of inferential network monitoring or network tomography subsequent to 339 sampling of the network state. Thus, network tomography enables the 340 extraction of a richer set of topology information, but at the same 341 time inherently increasing complexity of a potential information 342 plane and introducing estimation errors. For both active and passive 343 methods statistical models for the measurement process need to be 344 developed and the spatial and temporal dependence of the measurements 345 should be assessed. Moreover, measurement methodology and 346 statistical inference strategy must be considered jointly. For a 347 deeper discussion of network tomography and recent developments in 348 the field we refer the reader to [Coates]. 350 One system providing such a service is iPlane [Madhyastha], which 351 aims at creating a annotated atlas of the Internet that contains 352 information about latency, bandwidth, capacity and loss rate. To 353 determine features of the Internet topology, iPlane bridges and 354 builds upon different ideas, such as active probing based on packet 355 dispersion techniques to infer available bandwidth along path 356 segments. These ideas are drawn from different fields, including 357 network measurement as described by Dovrolis et al. in [Dovrolis] and 358 network tomography [Coates]. 360 2.2. Topology Estimation through Layer Cooperation 362 Instead of estimating topology information on the application level 363 through distributed measurements, this information could be provided 364 by the entities running the physical networks -- usually ISPs or 365 network operators. In fact, they have full knowledge of the topology 366 of the networks they administer and, in order to avoid congestion on 367 critical links, are interested in helping applications to optimize 368 the traffic they generate. The remainder of this section briefly 369 describes three recently proposed solutions that follow such an 370 approach to address the ALTO problem. 372 2.2.1. P4P Architecture 374 The architecture proposed by Xie et al. [Xie] has been adopted by 375 the DCIA P4P working group [P4P], an open group established by ISPs, 376 P2P software distributors and technology researchers with the dual 377 goal of defining mechanisms to accelerate content distribution and 378 optimize utilization of network resources. 380 The main role in the P4P architecture is played by servers called 381 "iTrackers", deployed by network providers and accessed by P2P 382 applications (or, in general, by elements of the P2P system) in order 383 to make optimal decisions when selecting a peer to connect. An 384 iTracker may offer three interfaces: 386 1. Info: Allows P2P elements (e.g. peers or trackers) to get opaque 387 information associated to an IP address. Such information is 388 kept opaque to hide the actual network topology, but can be used 389 to compute the network distance between IP addresses. 390 2. Policy: Allows P2P elements to obtain policies and guidelines of 391 the network, which specify how a network provider would like its 392 networks to be utilized at a high level, regardless of P2P 393 applications. 394 3. Capability: Allows P2P elements to request network providers' 395 capabilities. 397 The P4P architecture is under evaluation with simulations, 398 experiments on the PlanetLab distributed testbed and in field tests 399 with real users. Initial simulations and PlanetLab experiments 400 results [P4P] indicate that improvements in BitTorrent download 401 completion time and link utilization in the range of 50-70% are 402 possible. Results observed on Comcast's network during a field test 403 trial conducted with a modified version of the software used by the 404 Pando content delivery network (documented in RFC 5632 [RFC5632]) 405 show average improvements in download rate in different scenarios 406 varying between 57% and 85%, and a 34% to 80% drop in the cross- 407 domain traffic generated by such an application. 409 2.2.2. Oracle-based ISP-P2P Collaboration 411 In the general solution proposed by Aggarwal et al. [Aggarwal], 412 network providers host servers, called "oracles", that help P2P users 413 choose optimal neighbours. 415 The oracle concept uses the following mechanism: a P2P client sends 416 the list of potential peers to the oracle hosted by its ISP and 417 receives a re-arranged peer list, ordered according to the ISP's 418 local routing policies and preferences. For instance, to keep the 419 traffic local, the ISP may prefer peers within its network, or it may 420 pick links with higher bandwidth or peers that are geographically 421 closer to improve application performance. Once the client has 422 obtained this ordered list, it has enough information to perform 423 better-than-random initial peer selection. 425 Such a solution has been evaluated with simulations and experiments 426 run on the PlanetLab testbed and the results show both improvements 427 in content download time and a reduction of overall P2P traffic, even 428 when only a subset of the applications actually query the oracle to 429 make their decisions. 431 2.2.3. ISP-Driven Informed Path Selection (IDIPS) Service 433 The solution proposed by Saucez et al. [Saucez] is essentially a 434 modified version of the oracle-based approach described in 435 Section 2.2.2, intended to provide a network-layer service for 436 finding best source and destination addresses when establishing a 437 connection between two endpoints in multi-homed environments (which 438 are common in IPv6 networking). Peer selection optimization in P2P 439 systems -- the ALTO problem in today's Internet -- can be addressed 440 by the IDIPS solution as a specific sub-case where the options for 441 the destination address consist of all the peers sharing a desired 442 resource, while the choice of the source address is fixed. An 443 evaluation performed on IDIPS shows that costs for both providing and 444 accessing the service are negligible. 446 3. Application-Level Topology Estimation and the ALTO Problem 448 The application-level techniques described in Section Section 2.1 449 provide tools for peer-to-peer applications to estimate parameters of 450 the underlying network topology. Although these techniques can 451 improve application performance, there are limitations of what can be 452 achieved by operating only on the application level. 454 Topology estimation techniques use abstractions of the network 455 topology which often hide features that would be of interest to the 456 application. Network coordinate systems, for example, are unable to 457 detect overlay paths shorter than the direct path in the Internet 458 topology. However, these paths frequently exist in the Internet 459 [Wang]. Similarly, application-level techniques may not accurately 460 estimate topologies with multipath routing. 462 When using network coordinates to estimate topology information the 463 underlying assumption is that distance in terms of latency determines 464 performance. However, for file sharing and content distribution 465 applications there is more to performance than just the network 466 latency between nodes. The utility of a long-lived data transfer is 467 determined by the throughput of the underlying TCP protocol, which 468 depends on the round-trip time as well as the loss rate experienced 469 on the corresponding path [Padhye]. Hence, these applications 470 benefit from a richer set of topology information that goes beyond 471 latency including loss rate, capacity and available bandwidth. 473 Some of the topology estimation techniques used by P2P applications 474 need time to converge to a result. For example, current BitTorrent 475 clients implement local, passive traffic measurements and a tit-for- 476 tat bandwidth reciprocity mechanism to optimize peer selection at a 477 local level. Peers eventually settle on a set of neighbors that 478 maximizes their download rate but because peers cannot reason about 479 the value of neighbors without actively exchanging data with them and 480 the number of concurrent data transfers is limited (typically to 481 5-7), convergence is delayed and easily can be sub-optimal. 483 Skype's P2P Voice over IP (VoIP) application chooses a relay node in 484 cases where two peers are behind NATs and cannot connect directly. 485 Ren et al. [Ren] measured that the relay selection mechanism of 486 Skype is (1) not able to discover the best possible relay nodes in 487 terms of minimum RTT, (2) requires a long setup and stabilization 488 time which degrades the end user experience, and (3) is creating a 489 non-negligible amount of overhead traffic due to probing a large 490 number of nodes. They further showed that the quality of the relay 491 paths could be improved when the underlying network Autonomous System 492 (AS) topology is considered. 494 Some features of the network topology are hard to infer through 495 application-level techniques and it may not be possible to infer them 496 at all. An example for such a feature are service provider policies 497 and preferences such as the state and cost associated with 498 interdomain peering and transit links. Another example is the 499 traffic engineering policy of a service provider, which may 500 counteract the routing objective of the overlay network leading to a 501 poor overall performance [Seetharaman]. 503 Finally, application-level techniques often require applications to 504 perform measurements on the topology. These measurements create 505 traffic overhead, in particular, if measurements are performed 506 individually by all applications interested in estimating topology. 508 4. Open Issues 510 Beyond a significant amount of research work on the topic, we believe 511 that there are sizable open issues to address in an infrastructure- 512 based approach to traffic optimization. The following is not an 513 exhaustive list, but a representative sample of the pertinent issues. 515 4.1. Coordinate estimation or path latencies? 517 Despite the many solutions that have been proposed for providing 518 applications with topology information in a fully distributed manner, 519 there is currently an ongoing debate in the research community 520 whether such solutions should focus on estimating nodes' coordinates 521 or path latencies. Such a debate has recently been fed by studies 522 showing that the triangle inequality on which coordinate systems are 523 based is often proved false in the Internet [Ledlie]. Proposed 524 systems following both approaches -- in particular, Vivaldi [Dabek] 525 and PIC [Costa] following the former, Meridian [Wong] and iPlane 526 [Madhyastha] the latter -- have been simulated, implemented and 527 studied in real-world trials, each one showing different points of 528 strength and weaknesses. Concentrated work will be needed to 529 determine which of the two solutions will be conducive to the {ALTO} 530 problem. 532 4.2. Malicious nodes 534 Another open issue common in most distributed environments consisting 535 of a large number of peers is the resistance against malicious nodes. 536 Security mechanisms to identify misbehavior are based on triangle 537 inequality checks [Costa], which however tend to fail and thus return 538 false positives in presence of measurement inaccuracies induced, for 539 example, by traffic fluctuations that occur quite often in large 540 networks [Ledlie]. Beyond the issue of using triangle inequality 541 checks, authoritatively authenticating the identity of an oracle, and 542 preventing an oracle from attacks are also important. Exploration of 543 existing techniques -- such as Public Key Infrastructure (PKI) 544 [RFC5280] or identity-based encryption [Boneh] for authenticating the 545 identity and the use of secure multi-party computation techniques to 546 prevent an oracle from collusion attacks -- need to be studied for 547 judicious use in ALTO-type of solutions. 549 4.3. Information integrity 551 Similarly, even in controlled architectures deployed by network 552 operators where system elements may be authenticated [Xie], 553 [Aggarwal],[Saucez], it is still possible that the information 554 returned to applications is deliberately altered, for example, 555 assigning higher priority to financially inexpensive links instead of 556 neutrally applying proximity criteria. What are the effects of such 557 deliberate alterations if multiple peers collude to determine a 558 different route to the target, one that is not provided by an oracle? 559 Similarly, what are the consequences if an oracle targets a 560 particular node in another AS by redirecting an inordinate number of 561 querying peers to it causing, essentially, a Distributed Denial of 562 Service (DDoS) [RFC4732] attack on the node? Furthermore, does an 563 oracle broadcast or multicast a response to a query? If so, 564 techniques to protect the confidentiality of the multi-cast stream 565 will need to be investigated to thwart "free riding" peers. 567 4.4. Richness of topological information 569 Many systems already use RTT to account for delay when establishing 570 connections with peers (e.g., CAN [Ratnasamy], Bamboo [Rhea]). An 571 operator can provide not only the delay metric but other metrics that 572 the peer cannot figure out on its own. These metrics may include the 573 characteristics of the access links to other peers, bandwidth 574 available to peers (based on operator's engineering of its network), 575 network policies, and preferences such as state and cost associated 576 with intradomain peering links, and so on. Exactly what kinds of 577 metrics can an operator provide to stabilize the network throughput 578 will also need to be investigated. 580 4.5. Hybrid solutions 582 It is conceivable that P2P users may not be comfortable with operator 583 intervention to provide topology information. To eliminate this 584 intervention, alternative schemes to estimate topological distance 585 can be used. For instance, Ono uses client redirections generated by 586 Akamai CDN servers as an approximation for estimating distance to 587 peers; Vivaldi, GNP and PIC use synthetic coordinate systems. A 588 neutral third-party can make available a hybrid layer cooperation 589 service -- without the active participation of the ISP -- that uses 590 alternative techniques discussed in Section 2.1 to create a 591 topological map. This map can be subsequently used by a subset of 592 users who may not trust the ISP. 594 4.6. Negative impact of over-localization 596 The literature presented in Section 2 shows that a certain level of 597 locality-awareness in the peer selection process of P2P algorithms is 598 usually beneficial to the application performance. However, an 599 excessive localization of the traffic might cause partitioning in the 600 overlay interconnecting peers, which will negatively affect the 601 performance experienced by the peers themselves. 603 Finding the right balance between localization and randomness in peer 604 selection is an open issue. At the time of writing, it seems that 605 different applications have different levels of tolerance and should 606 be addressed separately. Le Blond et al. [LeBlond] have studied the 607 specific case of BitTorrent, proposing a simple mechanism to prevent 608 partitioning in the overlay, yet reaching a high level of cross- 609 domain traffic reduction without adversely impacting peers. 611 5. Security Considerations 613 This draft is a survey of existing literature on topology estimation. 614 As such, it does not introduce any new security considerations to be 615 taken into account beyond what is already discussed in each paper 616 surveyed. 618 6. IANA Considerations 620 None. 622 7. Acknowledgments 624 This document is a derivative work of a position paper submitted at 625 the IETF RAI area/MIT workshop held on May 28th, 2008 on the topic of 626 Peer-to-Peer Infrastructure (P2Pi) [RFC5594]. The article on a 627 similar topic from the same authors published in IEEE Communications 628 [Gurbani] was also partially derived from the same position paper. 629 The authors thank profusely Arnaud Legout, Richard Yang, Richard 630 Woundy, Stefano Previdi and the many people that have participated in 631 discussions and provided insightful feedback at any stage of this 632 work. 634 8. Informative References 636 [Aggarwal] 637 Aggarwal, V., Feldmann, A., and C. Scheidler, "Can ISPs 638 and P2P systems co-operate for improved performance?", 639 in ACM SIGCOMM Computer Communications Review, vol. 37, 640 no. 3. 642 [Azureus] "Azureus BitTorrent Client", . 644 [Boneh] Boneh, D. and M. Franklin, "Identity-Based Encryption from 645 the Weil Pairing", in proceedings of the 21st Annual 646 International Cryptology Conference on Advances in 647 Cryptology, August 2001. 649 [Coates] Coates, M., Hero, A., Nowak, R., and B. Yu, "Internet 650 Tomography", in IEEE Signal Processing Magazine, vol. 19, 651 no. 3. 653 [Costa] Costa, M., Castro, M., Rowstron, A., and P. Key, "PIC: 654 Practical Internet coordinates for distance estimation", 655 in proceedings of International Conference on Distributed 656 Systems 2003. 658 [Dabek] Dabek, F., Cox, R., Kaashoek, F., and R. Morris, "Vivaldi: 659 A Decentralized Network Coordinate System", in ACM 660 SIGCOMM: Proceedings of the 2004 conference on 661 Applications, technologies, architectures, and protocols 662 for computer communications, vol. 34, no. 4. 664 [Dovrolis] 665 Dovrolis, C., Ramanathan, P., and D. Moore, "What do 666 packet dispersion techniques measure?", in proceedings of 667 IEEE INFOCOM 2001. 669 [Francis] Francis, P., Jamin, S., Jin, C., Jin, Y., Raz, D., 670 Shavitt, Y., and L. Zhang, "IDMaps: A global Internet host 671 distance estimation service", in proceedings of IEEE 672 INFOCOM 2001. 674 [Glasner] Glasner, J., "P2P fuels global bandwidth binge", available 675 from http://www.wired.com/. 677 [Gummadi] Gummadi, K., Gummadi, R., Gribble, S., Ratnasamy, S., 678 Shenker, S., and I. Stoica, "The impact of DHT routing 679 geometry on resilience and proximity", in ACM SIGCOMM: 680 Proceedings of the 2003 conference on Applications, 681 technologies, architectures, and protocols for computer 682 communications. 684 [Gurbani] Gurbani, V., Hilt, V., Rimac, I., Tomsu, M., and E. 685 Marocco, "A Survey of Research on the Application-Layer 686 Traffic Optimization Problem and the Need for Layer 687 Cooperation", in IEEE Communications, vol. 47, no. 8. 689 [Karagiannis] 690 Karagiannis, T., Broido, A., Brownlee, N., Claffy, K., and 691 M. Faloutsos, "Is P2P dying or just hiding?", 692 in proceedings of IEEE GLOBECOM 2004 Conference. 694 [LeBlond] Le Blond, S., Legout, A., and W. Dabbous, "Pushing 695 BitTorrent Locality to the Limit", available 696 at http://hal.inria.fr/. 698 [Ledlie] Ledlie, J., Gardner, P., and M. Seltzer, "Network 699 Coordinates in the Wild", in USENIX: Proceedings of NSDI 700 2007. 702 [LightReading] 703 LightReading, "Controlling P2P traffic", available 704 from http://www.lightreading.com/. 706 [LinuxReviews] 707 linuxReviews.org, "Peer to peer network traffic may 708 account for up to 85% of Interneta??s bandwidth usage", 709 available from http://linuxreviews.org/. 711 [Madhyastha] 712 Madhyastha, H., Isdal, T., Piatek, M., Dixon, C., 713 Anderson, T., Krishnamurthy, A., and A. Venkataramani., 714 "iPlane: an information plane for distributed services", 715 in USENIX: Proceedings of the 7th symposium on Operating 716 systems design and implementation. 718 [Meeker] Meeker, M. and D. Joseph, "The State of the Internet, Part 719 3", available from http://www.morganstanley.com/. 721 [Ng] Ng, T. and H. Zhang, "Predicting internet network distance 722 with coordinates-based approaches", in proceedings of 723 INFOCOM 2002. 725 [Ono] "Northwestern University Ono Project", 726 . 729 [P4P] "DCIA P4P Working group", 730 . 732 [Padhye] Padhye, J., Firoiu, V., Towsley, D., and J. Kurose, 733 "Modeling TCP throughput: A simple model and its empirical 734 validation", in Technical Report UM-CS-1998-008, 735 University of Massachusetts 1998. 737 [Parker] Parker, A., "The true picture of peer-to-peer 738 filesharing", available from http://www.cachelogic.com/. 740 [RFC4732] Handley, M., Rescorla, E., and IAB, "Internet Denial-of- 741 Service Considerations", RFC 4732, December 2006. 743 [RFC4949] Shirey, R., "Internet Security Glossary, Version 2", 744 RFC 4949, August 2007. 746 [RFC4981] Risson, J. and T. Moors, "Survey of Research towards 747 Robust Peer-to-Peer Networks: Search Methods", RFC 4981, 748 September 2007. 750 [RFC5280] Cooper, D., Santesson, S., Farrell, S., Boeyen, S., 751 Housley, R., and W. Polk, "Internet X.509 Public Key 752 Infrastructure Certificate and Certificate Revocation List 753 (CRL) Profile", RFC 5280, May 2008. 755 [RFC5594] Peterson, J. and A. Cooper, "Report from the IETF Workshop 756 on Peer-to-Peer (P2P) Infrastructure, May 28, 2008", 757 RFC 5594, July 2009. 759 [RFC5632] Griffiths, C., Livingood, J., Popkin, L., Woundy, R., and 760 Y. Yang, "Comcast's ISP Experiences in a Proactive Network 761 Provider Participation for P2P (P4P) Technical Trial", 762 RFC 5632, September 2009. 764 [RFC5693] Seedorf, J. and E. Burger, "Application-Layer Traffic 765 Optimization (ALTO) Problem Statement", RFC 5693, 766 October 2009. 768 [Ratnasamy] 769 Ratnasamy, S., Francis, P., Handley, M., Karp, R., and S. 770 Shenker, "A Scalable Content-Addressable Network", in ACM 771 SIGCOMM: Proceedings of the 2001 conference on 772 Applications, technologies, architectures, and protocols 773 for computer communications, January 2001. 775 [Ren] Ren, S., Guo, L., and X. Zhang, "ASAP: An AS-aware peer- 776 relay protocol for high quality VoIP", in proceedings of 777 IEEE ICDCS 2006. 779 [Rhea] Rhea, S., Godfrey, B., Karp, B., Kubiatowicz, J., 780 Ratnasamy, S., Shenker, S., Stoica, I., and H. Yu, 781 "OpenDHT: a public DHT service and its uses", in ACM 782 SIGCOMM: Proceedings of the 2005 conference on 783 Applications, technologies, architectures, and protocols 784 for computer communications, August 2005. 786 [Saucez] Saucez, D., Donnet, B., and O. Bonaventure, 787 "Implementation and Preliminary Evaluation of an ISP- 788 Driven Informed Path Selection", in proceedings of ACM 789 CoNEXT 2007. 791 [Seetharaman] 792 Seetharaman, S., Hilt, V., Hofmann, M., and M. Ammar, 793 "Preemptive Strategies to Improve Routing Performance of 794 Native and Overlay Layers", in proceedings of IEEE INFOCOM 795 2007. 797 [Su] Su, A., Choffnes, D., Kuzmanovic, A., and F. Bustamante, 798 "Drafting behind Akamai (travelocity-based detouring)", 799 in ACM SIGCOMM: Proceedings of the 2006 conference on 800 Applications, technologies, architectures, and protocols 801 for computer communications. 803 [Wang] Wang, G., Zhang, B., and T. Ng, "Towards Network Triangle 804 Inequality Violation Aware Distributed Systems", in ACM 805 SIGCOMM: Proceedings of the 7th conference on Internet 806 measurement. 808 [Wong] Wong, B., Slivkins, A., and E. Sirer, "Meridian: A 809 lightweight network location service without virtual 810 coordinates", in ACM SIGCOMM: Proceedings of the 2005 811 conference on Applications, technologies, architectures, 812 and protocols for computer communications. 814 [Xie] Xie, H., Yang, Y., Krishnamurthy, A., Liu, Y., and A. 815 Silberschatz, "P4P: Explicit Communications for 816 Cooperative Control Between P2P and Network Providers", 817 in ACM SIGCOMM Computer Communication Review, vol. 38, no. 818 4. 820 Authors' Addresses 822 Ivica Rimac 823 Bell Labs, Alcatel-Lucent 825 Email: rimac@bell-labs.com 827 Volker Hilt 828 Bell Labs, Alcatel-Lucent 830 Email: volkerh@bell-labs.com 832 Marco Tomsu 833 Bell Labs, Alcatel-Lucent 835 Email: marco.tomsu@alcatel-lucent.com 836 Vijay K. Gurbani 837 Bell Labs, Alcatel-Lucent 839 Email: vkg@bell-labs.com 841 Enrico Marocco 842 Telecom Italia 844 Email: enrico.marocco@telecomitalia.it