idnits 2.17.1 draft-song-opsawg-ifit-framework-13.txt: Checking boilerplate required by RFC 5378 and the IETF Trust (see https://trustee.ietf.org/license-info): ---------------------------------------------------------------------------- No issues found here. Checking nits according to https://www.ietf.org/id-info/1id-guidelines.txt: ---------------------------------------------------------------------------- No issues found here. Checking nits according to https://www.ietf.org/id-info/checklist : ---------------------------------------------------------------------------- No issues found here. Miscellaneous warnings: ---------------------------------------------------------------------------- == The copyright year in the IETF Trust and authors Copyright Line does not match the current year == The document doesn't use any RFC 2119 keywords, yet seems to have RFC 2119 boilerplate text. -- The document date (October 5, 2020) is 1297 days in the past. Is this intentional? Checking references for intended status: Informational ---------------------------------------------------------------------------- -- Looks like a reference, but probably isn't: '1' on line 1229 == Outdated reference: A later version (-03) exists of draft-brockners-opsawg-ioam-deployment-02 == Outdated reference: A later version (-03) exists of draft-herbert-ipv4-eh-01 == Outdated reference: A later version (-17) exists of draft-ietf-ippm-ioam-data-10 == Outdated reference: A later version (-11) exists of draft-ietf-ippm-ioam-direct-export-01 == Outdated reference: A later version (-13) exists of draft-ietf-opsawg-ntf-04 == Outdated reference: A later version (-15) exists of draft-mirsky-ippm-hybrid-two-step-05 == Outdated reference: A later version (-16) exists of draft-song-ippm-postcard-based-telemetry-07 == Outdated reference: A later version (-13) exists of draft-song-mpls-extension-header-02 == Outdated reference: A later version (-07) exists of draft-song-multicast-telemetry-04 == Outdated reference: A later version (-10) exists of draft-wwx-netmod-event-yang-09 == Outdated reference: A later version (-14) exists of draft-zhou-ippm-enhanced-alternate-marking-05 Summary: 0 errors (**), 0 flaws (~~), 13 warnings (==), 2 comments (--). Run idnits with the --verbose option for more detailed information about the items above. -------------------------------------------------------------------------------- 2 OPSAWG H. Song 3 Internet-Draft Futurewei 4 Intended status: Informational F. Qin 5 Expires: April 8, 2021 China Mobile 6 H. Chen 7 China Telecom 8 J. Jin 9 LG U+ 10 J. Shin 11 SK Telecom 12 October 5, 2020 14 In-situ Flow Information Telemetry 15 draft-song-opsawg-ifit-framework-13 17 Abstract 19 As networks increase in scale and network operations become more 20 sophisticated, traditional Operation, Administration and Maintenance 21 (OAM) methods, which include proactive and reactive techniques, 22 running in active and passive modes, are no longer sufficient to meet 23 the monitoring and measurement requirements. Emerging on-path 24 telemetry techniques which provide high-precision flow insight and 25 real-time issue notification are required to ensure suitable quality 26 of experience for users and applications, and identify faults or 27 network deficiencies before they become critical. 29 This document outlines a high-level framework to provide an 30 operational environment that utilizes existing and emerging on-path 31 telemetry techniques to enable the collection and correlation of 32 performance information from the network. The framework identifies 33 the components that are needed to coordinate the existing protocol 34 tools and telemetry mechanisms, and addresses key deployment 35 challenges for flow-oriented on-path telemetry techniques, especially 36 in carrier networks. 38 The framework is informational and intended to guide system designers 39 attempting to use the referenced techniques as well as to motivate 40 further work to enhance the ecosystem . 42 Status of This Memo 44 This Internet-Draft is submitted in full conformance with the 45 provisions of BCP 78 and BCP 79. 47 Internet-Drafts are working documents of the Internet Engineering 48 Task Force (IETF). Note that other groups may also distribute 49 working documents as Internet-Drafts. The list of current Internet- 50 Drafts is at https://datatracker.ietf.org/drafts/current/. 52 Internet-Drafts are draft documents valid for a maximum of six months 53 and may be updated, replaced, or obsoleted by other documents at any 54 time. It is inappropriate to use Internet-Drafts as reference 55 material or to cite them other than as "work in progress." 57 This Internet-Draft will expire on April 8, 2021. 59 Copyright Notice 61 Copyright (c) 2020 IETF Trust and the persons identified as the 62 document authors. All rights reserved. 64 This document is subject to BCP 78 and the IETF Trust's Legal 65 Provisions Relating to IETF Documents 66 (https://trustee.ietf.org/license-info) in effect on the date of 67 publication of this document. Please review these documents 68 carefully, as they describe your rights and restrictions with respect 69 to this document. Code Components extracted from this document must 70 include Simplified BSD License text as described in Section 4.e of 71 the Trust Legal Provisions and are provided without warranty as 72 described in the Simplified BSD License. 74 Table of Contents 76 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3 77 1.1. Classification and Modes of On-path Telemetry . . . . . . 4 78 1.2. Requirements and Challenges . . . . . . . . . . . . . . . 6 79 1.3. Scope . . . . . . . . . . . . . . . . . . . . . . . . . . 8 80 1.4. Glossary . . . . . . . . . . . . . . . . . . . . . . . . 8 81 1.5. Requirements Language . . . . . . . . . . . . . . . . . . 9 82 2. IFIT Overview . . . . . . . . . . . . . . . . . . . . . . . . 9 83 2.1. Typical Deployment of IFIT . . . . . . . . . . . . . . . 10 84 2.2. IFIT Architecture . . . . . . . . . . . . . . . . . . . . 11 85 2.3. Relationship with Network Telemetry Framework (NTF) . . . 12 86 3. Key Components of IFIT . . . . . . . . . . . . . . . . . . . 13 87 3.1. Flexible Flow, Packet, and Data Selection . . . . . . . . 13 88 3.1.1. Block Diagram . . . . . . . . . . . . . . . . . . . . 14 89 3.1.2. Example: Sketch-guided Elephant Flow Selection . . . 14 90 3.1.3. Example: Adaptive Packet Sampling . . . . . . . . . . 14 91 3.2. Flexible Data Export . . . . . . . . . . . . . . . . . . 15 92 3.2.1. Block Diagram . . . . . . . . . . . . . . . . . . . . 15 93 3.2.2. Example: Event-based Anomaly Monitor . . . . . . . . 16 94 3.3. Dynamic Network Probe . . . . . . . . . . . . . . . . . . 17 95 3.3.1. Block Diagram . . . . . . . . . . . . . . . . . . . . 17 96 3.3.2. Examples . . . . . . . . . . . . . . . . . . . . . . 18 98 3.4. On-demand Technique Selection and Integration . . . . . . 18 99 3.4.1. Block Diagram . . . . . . . . . . . . . . . . . . . . 19 100 4. IFIT for Reflective Telemetry . . . . . . . . . . . . . . . . 19 101 4.1. Example: Intelligent Multipoint Performance Monitoring . 20 102 4.2. Example: Intent-based Network Monitoring . . . . . . . . 21 103 5. Standard Status and Gaps . . . . . . . . . . . . . . . . . . 22 104 5.1. Encapsulation in Transport Protocols . . . . . . . . . . 22 105 5.2. Tunneling Support . . . . . . . . . . . . . . . . . . . . 22 106 5.3. Deployment Automation . . . . . . . . . . . . . . . . . . 22 107 6. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 108 7. Security Considerations . . . . . . . . . . . . . . . . . . . 24 109 8. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 24 110 9. Contributors . . . . . . . . . . . . . . . . . . . . . . . . 24 111 10. Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . 24 112 11. References . . . . . . . . . . . . . . . . . . . . . . . . . 24 113 11.1. Normative References . . . . . . . . . . . . . . . . . . 24 114 11.2. Informative References . . . . . . . . . . . . . . . . . 25 115 11.3. URIs . . . . . . . . . . . . . . . . . . . . . . . . . . 27 116 Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 27 118 1. Introduction 120 Efficient network operation increasingly relies on high-quality data- 121 plane telemetry to provide the necessary visibility. Traditional 122 Operation, Administration and Maintenance (OAM) methods, which 123 include proactive and reactive techniques, running both active and 124 passive modes, are no longer sufficient to meet the monitoring and 125 measurement requirements. The complexity of today's networks and 126 service quality requirements demand new high-precision and real-time 127 techniques. 129 The ability to expedite network failure detection, fault 130 localization, and recovery mechanisms, particularly in the case of 131 soft failures or path degradation is expected, without causing 132 service disruption. Networks also become application-aware. 133 Application-aware networking is an industry term used to describe the 134 capacity of a network to maintain current information about users and 135 application connections which may be used to optimize the network 136 resource usage and improve the quality of service. 138 The emerging on-path telemetry techniques can provide high-precision 139 flow insight and real-time network issue notification (e.g., jitter, 140 latency, packet loss, significant bit error variations, and unequal 141 load-balancing). On-path telemetry refers to the data-plane 142 telemetry techniques that directly tap and measure network traffic by 143 embedding instructions or metadata into user packets. The data 144 provided by on-path telemetry are especially useful for SLA 145 compliance, user experience enhancement, service path enforcement, 146 fault diagnosis, and network resource optimization. It is essential 147 to recognize that existing work on this topic includes a variety of 148 on-path telemetry techniques, including In-situ OAM (IOAM) 149 [I-D.ietf-ippm-ioam-data], Postcard-based Telemetry (PBT) 150 [I-D.song-ippm-postcard-based-telemetry], Enhanced Alternate Marking 151 (EAM) [I-D.zhou-ippm-enhanced-alternate-marking], and Hybrid Two 152 Steps (HTS) [I-D.mirsky-ippm-hybrid-two-step], have been proposed, 153 which can provide flow information on the entire forwarding path on a 154 per-packet basis in real-time. The aforementioned on-path telemetry 155 techniques differ from the active and passive OAM schemes discussed 156 earlier in that, they directly modify the user packets and can 157 guarantee 100% accuracy. These on-path telemetry techniques can be 158 classified as the OAM hybrid type I, since they involve "augmentation 159 or modification of the stream of interest, or employment of methods 160 that modify the treatment of the streams", according to [RFC7799]. 162 On-path telemetry is useful for application-aware networking 163 operations not only in data center and enterprise networks but also 164 in carrier networks which may cross multiple domains. Carrier 165 network operators have shown interest in utilizing such techniques 166 for various purposes. For example, it is critical for the operators 167 who offer high-bandwidth, latency and loss-sensitive services such as 168 video streaming and online gaming to closely monitor the relevant 169 flows in real-time as the basis for any further optimizations. 171 This framework document is intended to guide system designers 172 attempting to use the referenced techniques as well as to motivate 173 further work to enhance the telemetry ecosystem. It highlights 174 requirements and challenges, outlines vital techniques that are 175 applicable, and provides examples of how these might be applied for 176 critical use cases. 178 The document scope is discussed in Section 1.3. 180 1.1. Classification and Modes of On-path Telemetry 182 The operation of on-path telemetry differs from both active OAM and 183 passive OAM as defined in [RFC7799]. It does not generate any active 184 probe packets or passively observes unmodified user packets. 185 Instead, it modifies selected user packets to collect useful 186 information about them. Therefore, the operation can be categorized 187 as the hybrid OAM type I mode per [RFC7799], which can provide more 188 flexible and accurate network monitoring and measurement. 190 This hybrid type OAM can be further partitioned into two modes. 191 [passport-postcard] first uses the metaphor of "passport" and 192 "postcard" to describe how the on-path data can be collected and 193 exported. In the passport mode, each node on the path adds the 194 telemetry data to the user packets (i.e., stamp the passport). The 195 accumulated data trace is exported at a configured end node. In the 196 postcard mode, each node directly exports the telemetry data using an 197 independent packet (i.e., send a postcard) while the user packets are 198 intact. It is possible to combine the two modes together in one 199 solution. We call this the hybrid mode. 201 Figure 1 shows the classification of the existing on-path telemetry 202 techniques. 204 +-----------+--------------+--------------+---------------+ 205 | Mode | Passport | Postcard | Hybrid | 206 +-----------+--------------+--------------+---------------+ 207 | | IOAM Trace | IOAM DEX | Multicast Te- | 208 | Technique | IOAM E2E | PBT-M | lemetry | 209 | | EAM | | HTS | 210 +-----------+--------------+--------------+---------------+ 212 Figure 1: ON-path Telemetry Technique Classification 214 IOAM Trace and E2E options are described in 215 [I-D.ietf-ippm-ioam-data]. EAM is described in 216 [I-D.zhou-ippm-enhanced-alternate-marking]. IOAM DEX option is 217 described in [I-D.ietf-ippm-ioam-direct-export] PBT-M is described in 218 [I-D.song-ippm-postcard-based-telemetry]. Multicast Telemetry is 219 described in [I-D.song-multicast-telemetry]. HTS is described in 220 [I-D.mirsky-ippm-hybrid-two-step]. 222 The advantages of the passport mode include : 224 o It naturally retains the telemetry data correlation along the 225 entire path. The self-describing feature eases the data 226 consumption. 228 o The on-path data for a packet is only exported once so the data 229 export overhead is low. 231 o Only the head and end nodes of the paths need to be configured so 232 the configuration overhead is low. 234 The disadvantages of the passport mode include : 236 o The telemetry data carried by user packets inflate the packet 237 size, which is undesirable or prohibitive. 239 o Approaches for encapsulating the instruction header and data in 240 transport protocols need to be standardized, which is challenging. 242 o Carrying sensitive data along the path is vulnerable to security 243 and privacy breach. 245 o If a packet is dropped on the path, the data collected so far are 246 also lost. 248 The postcard mode provides a perfect complement to the passport mode 249 by addressing the issues of the passport mode. The advantages of the 250 postcard mode include: 252 o Either there is no packet header overhead (e.g., PBT-M) or the 253 overhead is small and fixed (e.g., IOAM DEX). 255 o The encapsulation requirement can be avoided (e.g., PBT-M). 257 o The telemetry data can be secured. 259 o Even if a packet is dropped on the path, the partial data 260 collected so far are still available. 262 The disadvantages of the postcard mode include: 264 o Telemetry data are spread in multiple postcards so extra effort is 265 needed to correlate the data. 267 o Every node exports a postcard for a packet which increases the 268 data export overhead. 270 o In case of PBT-M, every node on the path needs to be configured, 271 so the configuration overhead is high. 273 o In case of IOAM DEX, the encapsulation requirement remains. 275 The hybrid mode either tailors for some specific application scenario 276 (e.g., Multicast Telemetry) or provides some alternative approach 277 (e.g., HTS). 279 1.2. Requirements and Challenges 281 Although on-path telemetry is beneficial, successfully applying such 282 techniques in carrier networks must consider performance, 283 deployability, and flexibility. Specifically, we need to address the 284 following practical deployment challenges: 286 o C1: On-path telemetry incurs extra packet processing which may 287 cause stress on the network data plane. The potential impact on 288 the forwarding performance creates an unfavorable "observer 289 effect". This will not only damages the fidelity of the 290 measurement but also defies the purpose of the measurement. For 291 example, the growing IOAM data per hop can negatively affect 292 service levels by increasing the serialization delay and header 293 parsing delay. 295 o C2: On-path telemetry can generate a considerable amount of data 296 which may claim too much transport bandwidth and inundate the 297 servers for data collection, storage, and analysis. Increasing 298 the data handling capacity is technically viable but expensive. 299 For example, if IOAM is applied to all the traffic, one node may 300 collect a few tens of bytes as telemetry data for each packet. 301 The whole forwarding path might accumulate a data-trace with a 302 size similar to or even exceeding that of the original packet. 303 Transporting the telemetry data alone is projected to consume 304 almost half of the network bandwidth, plus it creates significant 305 back-end data handling and storage requirements. 307 o C3: The collectible data defined currently are essential but 308 limited. As the network operation evolves to be declarative 309 (intent-based) and automated, and the trends of network 310 virtualization, wireline and wireless convergence, and packet- 311 optical integration continue, more data is needed in an on-demand 312 and interactive fashion. Flexibility and extensibility on data 313 defining, aggregation, acquisition, and filtering, must be 314 considered. 316 o C4: Applying only a single underlying on-path telemetry technique 317 may lead to a defective result. For example, packet drop can 318 cause the loss of the flow telemetry data and the packet drop 319 location and reason remains unknown if only the In-situ OAM trace 320 option is used. A comprehensive solution needs the flexibility to 321 switch between different underlying techniques and adjust the 322 configurations and parameters at runtime. Thus, system-level 323 orchestration is needed. 325 o C5: If we were to apply some on-path telemetry technique in 326 today's carrier operator networks, we must provide solutions to 327 tailor the provider's network deployment base and support an 328 incremental deployment strategy. That is, we need to support 329 established encapsulation schemes for various predominant 330 protocols such as Ethernet, IPv4, IPv6, and MPLS with backward 331 compatibility and properly handle various transport tunnels. 333 o C6: The development of simplified on-path telemetry primitives and 334 models for configuration and queries is essential. Telemetry 335 models may be utilized via an API-based telemetry service for 336 external applications, for end-to-end performance measurement and 337 application performance monitoring. The standard-based protocols 338 and methods are needed for network configuration and programming, 339 and telemetry data processing and export, to provide 340 interoperability. 342 1.3. Scope 344 Following the network telemetry framework discussed in 345 [I-D.ietf-opsawg-ntf], this document focuses on the on-path 346 telemetry, a specific class of data-plane telemetry techniques, and 347 provides a high-level framework which addresses the aforementioned 348 challenges for deployment, especially in carrier operator networks. 350 This document aims to clarify the problem space, essential 351 requirements, and summarizes best practices and general system design 352 considerations. The framework helps to analyze the current standard 353 status and identify gaps, and to motivate new standard works to 354 complete the ecosystem. This document provides some examples to show 355 some novel network telemetry applications under the framework. 357 As an informational document, it describes an open framework with a 358 few key components. The framework does not enforces any specific 359 implementation on each component, neither does it define interfaces 360 (e.g., API, protocol) between components. The choice of underlying 361 on-path telemetry techniques and other implementation details is 362 determined by application implementer. Therefore, the framework is 363 not a solution specification. It only provides a high-level overview 364 and is not necessarily a mandatory recommendation for on-path 365 telemetry applications. Implementation of the framework is 366 implementor specific and may utilize functional components and 367 techniques outlined in this document. 369 The standardization of the underlying techniques and interfaces 370 mentioned in this document is undertaken by various working groups. 371 Due to the limited scope and intended status of this document, it has 372 no overlap or conflict with those works. 374 1.4. Glossary 376 This section defines and explains the acronyms and terms used in this 377 document. 379 On-path Telemetry: Remotely acquiring performance and behavior data 380 about network flows on a per-packet basis on the packet's 381 forwarding path. The term refers to a class of data plane 382 telemetry techniques, including IOAM, PBT, EAM, and HTS. Such 383 techniques may need to mark user packets, or insert instruction or 384 metadata to the headers of user packets. 386 IFIT: In-situ Flow Information Telemetry, pronounced as "I-Fit". 387 The name of a high-level reference framework that shows how 388 network data-plane monitoring applications can address the 389 deployment challenges of the flow-oriented on-path telemetry 390 techniques. 392 IFIT Domain: A network domain in which an on-path telemetry 393 application operates. The network domain contains multiple 394 forwarding devices, such as routers and switches, that are capable 395 of IFIT-specific functions. It also contains a logically 396 centralized controller whose responsibility is to apply IFIT- 397 specific configurations and functions to IFIT-capable forwarding 398 devices, and to collect and analyze the on-path telemetry data 399 from those devices. An IFIT domain contains multiple network node 400 capable of IFIT-specific functions. We name all the entry nodes 401 to an IFIT domain head nodes and all the exit nodes end nodes. A 402 path in an IFIT domain starts from a head node and ends at an end 403 node. Usually the instruction header encapsulation or packet 404 marking, if needed, happens at the head nodes; the instruction 405 header decapsulation or packet unmarking, if needed, happens at 406 the end nodes. 408 Reflective Telemetry: The telemetry functions in a dynamic and 409 interactive fashion. A new telemetry action is provisioned as a 410 result of self-knowledge acquired through prior telemetry actions. 412 1.5. Requirements Language 414 The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", 415 "SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and 416 "OPTIONAL" in this document are to be interpreted as described in BCP 417 14 [RFC2119][RFC8174] when, and only when, they appear in all 418 capitals, as shown here. 420 2. IFIT Overview 422 To address the challenges mentioned above, we present a high-level 423 framework based on multiple network operators' requirements and 424 common industry practice, which can help to build a workable and 425 efficient on-path telemetry application. We name the framework "In- 426 situ Flow Information Telemetry" (IFIT) to reflect the fact that this 427 framework is dedicated to on-path telemetry data about user and 428 application traffic flows. As a reference framework, IFIT covers a 429 class of on-path telemetry techniques and works a level higher than 430 any specific underlying technique. The framework is comprised of 431 some key functional components (Section 3). By assembling these 432 components, IFIT supports reflective telemetry that enables 433 autonomous network operations (Section 4). 435 2.1. Typical Deployment of IFIT 437 Figure 2 shows a typical deployment scenario of IFIT. 439 Application 440 +-------------------------------------+ 441 | Controller | 442 | +------------+ +-----------+ | 443 | | Configure | | Collector | | 444 | | & |<-------| & | | 445 | | Control | | Analyzer | | 446 | +-----:------+ +-----------+ | 447 | : ^ | 448 +-------:---------------------|-------+ 449 :configuration |telemetry data 450 :& action | 451 ...............:.....................|.......... 452 : : : | : 453 : +---------:---+-------------:---++---------:---+ 454 : | : | : | : | 455 V | V | V | V | 456 +------+-+ +-----+--+ +------+-+ +------+-+ 457 packets| Head | | Path | | Path | | End | 458 ==>| Node |====>| Node |==//==>| Node |====>| Node |==> 459 | | | A | | B | | | 460 +--------+ +--------+ +--------+ +--------+ 462 |<--- IFIT Domain --->| 464 Figure 2: IFIT Deployment Scenario 466 An on-path telemetry application can conduct some network data plane 467 monitoring and measurement tasks over an IFIT domain by applying one 468 or more underlying techniques. The application needs to contains 469 multiple elements, including configuring the network nodes and 470 processing the telemetry data. The application usually runs in a 471 logically centralized controller which is responsible for configuring 472 the network nodes in the IFIT domain, and collecting and analyzing 473 telemetry data. The configuration determines which underlying 474 technique is used, what telemetry data are of interest, which flows 475 and packets are concerned with, how the telemetry data are collected, 476 etc. The process can be dynamic and interactive: after the telemetry 477 data processing and analyzing, the application may instruct the 478 controller to modify the configuration of the nodes in the IFIT 479 domain, which affects the future telemetry data collection. 481 From the system-level view, it is recommended to use the standardized 482 configuration and data collection interfaces, regardless of the 483 underlying technique. However, the specification of these interfaces 484 and the implementation of the controller are out of scope for this 485 document. 487 The IFIT domain encompasses the head nodes and the end nodes. An 488 IFIT domain may cross multiple network domains. The head nodes are 489 responsible for enabling the IFIT-specific functions and the end 490 nodes are responsible for terminating them. All capable nodes in an 491 IFIT domain will be capable of executing the instructed IFIT-specific 492 function. It is important to note that any IFIT application must, 493 through configuration and policy, guarantee that any packet with 494 IFIT-specific header and metadata will not leak out of the IFIT 495 domain. The end nodes must be able to capture all packets with IFIT- 496 specific header and metadata and recover their format before 497 forwarding them out of the IFIT domain. 499 The underlying on-path telemetry techniques covered by IFIT can be of 500 any modes discussed in Section 1.1. 502 2.2. IFIT Architecture 504 The IFIT architecture is shown in Figure 3, which contains several 505 key components. These components aim to address the deployment 506 challenges discussed in Section 1. The detailed block diagram and 507 description for each component are given in Section 3. Here we only 508 provide a high level overview. 510 +------------------------------------+ 511 | On-demand Technique | 512 | Selection & Integration | 513 +------------------------------------+ 514 Control Plane | ^ 515 ---------------------+-------------------+------------- 516 Forwarding Plane V | 517 +-----------------+------------------+ 518 | Flexible Flow, | Flexible | 519 | Packet, & Data | Data Export | 520 | Selection | | 521 +-----------------+------------------| 522 | Dynamic Network Probe | 523 +------------------------------------| 525 Figure 3: IFIT Architecture 527 Based on the monitoring and measurement requirements, an application 528 needs to choose one or more underlying on-path telemetry techniques 529 and decide the policies to apply them. Depending on the forwarding- 530 plane protocol and tunneling configuration, the instruction header 531 and metadata encapsulation method, if needed, is also determined. 532 The encapsulation happens at the head nodes and the decapsulation 533 happens at the end nodes. 535 Based on the network condition and application requirement, the head 536 nodes also need to be able to choose flows and packets to enable the 537 IFIT-specific functions, and decide the set of data to be collected. 538 All the nodes who are responsible for exporting telemetry data are 539 configured with special functions to prepare the data. The IFIT- 540 specific functions can be deployed into the network nodes as dynamic 541 network probes. 543 2.3. Relationship with Network Telemetry Framework (NTF) 545 [I-D.ietf-opsawg-ntf] describes a Network Telemetry Framework (NTF). 546 One dimension used by NTF to partition network telemetry techniques 547 and systems is based on the three planes in networks plus external 548 data sources. IFIT fits in the category of forwarding-plane 549 telemetry and deals with the specific on-path technical branch of the 550 forwarding-plane telemetry. 552 According to NTF, an on-path telemetry application mainly subscribes 553 event-triggered or streaming data. The key functional components of 554 IFIT also match the components in NTF. "On-demand Technique 555 Selection and Integration" is an application layer function, matching 556 the "Data Query, Analysis, and Storage" component in NTF; "Flexible 557 Flow, Packet, and Data Selection" matches the "Data Configuration and 558 Subscription" component; "Flexible Data Export" matches the "Data 559 Encoding and Export" component; "Dynamic Network Probe" matches the 560 "Data Generation and Processing" component. 562 3. Key Components of IFIT 564 As shown in the IFIT architecture, the key components of IFIT are as 565 follows: 567 o Flexible flow, packet, and data selection policy, addressing the 568 challenge C1 described in Section 1; 570 o Flexible data export, addressing the challenge C2; 572 o Dynamic network probe, addressing C3; 574 o On-demand technique selection and integration, addressing C4. 576 Note that the challenges C5 and C6 are mostly standard related, which 577 are fundamental to IFIT. We discuss the standard status and gaps in 578 Section 5. 580 In the following section, we provide a detailed description of each 581 component. 583 3.1. Flexible Flow, Packet, and Data Selection 585 In most cases, it is impractical to enable the data collection for 586 all the flows and for all the packets in a flow due to the potential 587 performance and bandwidth impact. Therefore, a workable solution 588 usually need to select only a subset of flows and flow packets to 589 enable the data collection, even though this means the loss of some 590 information and accuracy. 592 In the data plane, the Access Control List (ACL) provides an ideal 593 means to determine the subset of flow(s). An application can set a 594 sample rate or probability to a flow to allow only a subset of flow 595 packets to be monitored, collect a different set of data for 596 different packets, and disable or enable data collection on any 597 specific network node. An application can further allow any node to 598 accept or deny the data collection process in full or partially. 600 Based on these flexible mechanisms, IFIT allows applications to apply 601 flexible flow and data selection policies to suit the requirements. 602 The applications can dynamically change the policies at any time 603 based on the network load, processing capability, focus of interest, 604 and any other criteria. 606 3.1.1. Block Diagram 608 +----------------------------+ 609 | +----------+ +----------+ | 610 | |Flow | |Data | | 611 | |Selection | |Selection | | 612 | +----------+ +----------+ | 613 | +----------+ | 614 | |Packet | | 615 | |Selection | | 616 | +----------+ | 617 +----------------------------+ 619 Figure 4: Flexible Flow, Packet, and Data Selection 621 Figure 4 shows the block diagram of this component. The flow 622 selection block defines the policies to choose target flows for 623 monitoring. Flow has different granularity. A basic flow is defined 624 by 5-tuple IP header fields. Flow can also be aggregated at 625 interface level, tunnel level, protocol level, and so on. The packet 626 selection block defines the policies to choose packets from a target 627 flow. The policy can be either a sampling interval, a sampling 628 probability, or some specific packet signature. The data selection 629 block defines the set of data to be collected. This can be changed 630 on a per-packet or per-flow basis. 632 3.1.2. Example: Sketch-guided Elephant Flow Selection 634 Network operators are usually more interested in elephant flows which 635 consume more resource and are sensitive to changes in network 636 conditions. A CountMin Sketch [CMSketch] can be used on the data 637 path of the head nodes, which identifies and reports the elephant 638 flows periodically. The controller maintains a current set of 639 elephant flows and dynamically enables the on-path telemetry for only 640 these flows. 642 3.1.3. Example: Adaptive Packet Sampling 644 Applying on-path telemetry on all packets of selected flows can still 645 be out of reach. A sample rate should be set for these flows and 646 only enable telemetry on the sampled packets. However, the head 647 nodes have no clue on the proper sampling rate. An overly high rate 648 would exhaust the network resource and even cause packet drops; An 649 overly low rate, on the contrary, would result in the loss of 650 information and inaccuracy of measurements. 652 An adaptive approach can be used based on the network conditions to 653 dynamically adjust the sampling rate. Every node gives user traffic 654 forwarding higher priority than telemetry data export. In case of 655 network congestion, the telemetry can sense some signals from the 656 data collected (e.g., deep buffer size, long delay, packet drop, and 657 data loss). The controller may use these signals to adjust the 658 packet sampling rate. In each adjustment period (i.e., RTT of the 659 feedback loop), the sampling rate is either decreased or increased in 660 response of the signals. An AIMD policy similar to the TCP flow 661 control mechanism for the rate adjustment can be used. 663 3.2. Flexible Data Export 665 The flow telemetry data can catch the dynamics of the network and the 666 interactions between user traffic and network. Nevertheless, the 667 data inevitably contain redundancy. It is advisable to remove the 668 redundancy from the data in order to reduce the data transport 669 bandwidth and server processing load. 671 In addition to efficient export data encoding (e.g., IPFIX [RFC7011] 672 or protobuf [1]), nodes have several other ways to reduce the export 673 data by taking advantage of network device's capability and 674 programmability. Nodes can cache the data and send the accumulated 675 data in batch if the data is not time sensitive. Various 676 deduplication and compression techniques can be applied on the batch 677 data. 679 From the application perspective, an application may only be 680 interested in some special events which can be derived from the 681 telemetry data. For example, in case that the forwarding delay of a 682 packet exceeds a threshold, or a flow changes its forwarding path is 683 of interest, it is unnecessary to send the original raw data to the 684 data collecting and processing servers. Rather, IFIT takes advantage 685 of the in-network computing capability of network devices to process 686 the raw data and only push the event notifications to the subscribing 687 applications. 689 Such events can be expressed as policies. An policy can request data 690 export only on change, on exception, on timeout, or on threshold. 692 3.2.1. Block Diagram 693 +-------------------------------------------+ 694 | +-----------+ +-----------+ +-----------+ | 695 | |Data | |Data | |Export | | 696 | |Encoding | |Batching | |Protocol | | 697 | +-----------+ +-----------+ +-----------+ | 698 | +-----------+ +-----------+ +-----------+ | 699 | |Data | |Data | |Data | | 700 | |Compression| |Dedup. | |Filter | | 701 | +-----------+ +-----------+ +-----------+ | 702 | +-----------+ +-----------+ | 703 | |Data | |Data | | 704 | |Computing | |Aggregation| | 705 | +-----------+ +-----------+ | 706 +-------------------------------------------+ 708 Figure 5: Flexible Data Export 710 Figure 5 shows the block diagram of this component. The data 711 encoding block defines the method to encode the telemetry data. The 712 data batching block defines the size of batch data buffered at the 713 device side before export. The export protocol block defines the 714 protocol used for telemetry data export. The data compression block 715 defines the algorithm to compress the raw data. The data 716 deduplication block defines the algorithm to remove the redundancy in 717 the raw data. The data filter block defines the policies to filter 718 the needed data. The data computing block defines the policies to 719 prepocess the raw data and generate some new data. The data 720 aggregation block defines the procedure to combine and synthesize the 721 data. 723 3.2.2. Example: Event-based Anomaly Monitor 725 Network operators are interested in the anomalies such as path 726 change, network congestion, and packet drop. Such anomalies are 727 hidden in raw telemetry data (e.g., path trace, timestamp). Such 728 anomalies can be described as events and programmed into the device 729 data plane. Only the triggered events are exported. For example, if 730 a new flow appears at any node, a path change event is triggered; if 731 the packet delay exceeds a predefined threshold in a node, the 732 congestion event is triggered; if a packet is dropped due to buffer 733 overflow, a packet drop event is triggered. 735 The export data reduction due to such optimization is substantial. 736 For example, given a single 5-hop 10Gbps path, assume a moderate 737 number of 1 million packets per second are monitored, and the 738 telemetry data plus the export packet overhead consume less than 30 739 bytes per hop. Without such optimization, the bandwidth consumed by 740 the telemetry data can easily exceed 1Gbps (>10% of the path 741 bandwidth), When the optimization is used, the bandwidth consumed by 742 the telemetry data is negligible. Moreover, the pre-processed 743 telemetry data greatly simplify the work of data analyzers. 745 3.3. Dynamic Network Probe 747 Due to limited data plane resource and network bandwidth, it is 748 unlikely one can monitor all the data all the time. On the other 749 hand, the data needed by applications may be arbitrary but ephemeral. 750 It is critical to meet the dynamic data requirements with limited 751 resource. 753 Fortunately, data plane programmability allows IFIT to dynamically 754 load new data probes. These on-demand probes are called Dynamic 755 Network Probes (DNP). DNP is the technique to enable probes for 756 customized data collection in different network planes. When working 757 with IOAM or PBT, DNP is loaded to the data plane through incremental 758 programming or configuration. The DNP can effectively conduct data 759 generation, processing, and aggregation. 761 DNP introduces enough flexibility and extensibility to IFIT. It can 762 implement the optimizations for export data reduction motioned in the 763 previous section. It can also generate custom data as required by 764 today and tomorrow's applications. 766 3.3.1. Block Diagram 768 +----------------------------+ 769 | +----------+ +----------+ | 770 | |ACL | |YANG | | 771 | | | |Model | | 772 | +----------+ +----------+ | 773 | +----------+ +----------+ | 774 | |Hardware | |Software | | 775 | |Function | |Function | | 776 | +----------+ +----------+ | 777 +----------------------------+ 779 Figure 6: Dynamic Network Probes 781 Figure 6 shows the block diagram of this component. The Access 782 Control List (ACL) block is available in most hardware and it defines 783 DNPs through dynamically update the ACL policies (including flow 784 filtering and action). YANG models can be dynamically deployed to 785 enable different data processing and filtering functions. Some 786 hardware allows dynamically loading hardware-based functions into the 787 forwarding path at runtime through mechanisms such as reserved 788 pipelines and function stubs. Dynamically loadable software 789 functions can be implemented in the control processors in IFIT nodes. 791 3.3.2. Examples 793 Following are some possible DNPs that can be dynamically deployed to 794 support applications. 796 On-demand Flow Sketch: A flow sketch is a compact online data 797 structure (usually a variation of multi-hashing table) for 798 approximate estimation of multiple flow properties. It can be 799 used to facilitate flow selection. The aforementioned CountMin 800 Sketch [CMSketch] is such an example. Since a sketch consumes 801 data plane resources, it should only be deployed when actually 802 needed. 804 Smart Flow Filter: The policies that choose flows and packet 805 sampling rate can change during the lifetime of an application. 807 Smart Statistics: An application may need to count flows based on 808 different flow granularity or maintain hit counters for selected 809 flow table entries. 811 Smart Data Reduction: DNP can be used to program the events that 812 conditionally trigger data export. 814 3.4. On-demand Technique Selection and Integration 816 With multiple underlying data collection and export techniques at its 817 disposal, IFIT can flexibly adapt to different network conditions and 818 different application requirements. 820 For example, depending on the types of data that are of interest, 821 IFIT may choose either IOAM or PBT to collect the data; if an 822 application needs to track down where the packets are lost, switching 823 from IOAM to PBT should be supported. 825 IFIT can further integrate multiple data plane monitoring and 826 measurement techniques together and present a comprehensive data 827 plane telemetry solution. 829 Based on the application requirements and the real-time telemetry 830 data analysis results, new configurations and actions can be 831 deployed. 833 3.4.1. Block Diagram 835 +----------------------------------------------+ 836 | +------------+ +-------------+ +---------+ | 837 | |Application | |Configuration| |Telemetry| | 838 | |Requirements|->|& Action |<-|Data | | 839 | | | | | |Analysis | | 840 | +------------+ +-------------+ +---------+ | 841 +----------------------------------------------+ 842 | Passport Mode: | 843 | +----------+ +----------+ +----------+ | 844 | |IOAM E2E | |IOAM Trace| |EAM | | 845 | +----------+ +----------+ +----------+ | 846 | Postcard Mode: | 847 | +----------+ +----------+ | 848 | |PBT-M | |IOAM DEX | | 849 | +----------+ +----------+ | 850 | Hybrid Mode: | 851 | +----------+ +----------+ | 852 | |HTS | |Multicast | | 853 | | | |Telemetry | | 854 | +----------+ +----------+ | 855 +----------------------------------------------+ 857 Figure 7: Technique Selection and Integration 859 Figure 7 shows the block diagram of this component, which lists the 860 candidate on-path telemetry techniques. 862 Located in the logically centralized controller of an IFIT domain, 863 this component makes all the control and configuration dynamically to 864 the capable nodes in the domain which will affect the future 865 telemetry data. The configuration and action decisions are based on 866 the inputs from the application requirements and the realtime 867 telemetry data analysis results. Note that here the telemetry data 868 source is not limited to the data plane. The data can come form all 869 the sources mentioned in [I-D.ietf-opsawg-ntf], including external 870 data sources. 872 4. IFIT for Reflective Telemetry 874 The IFIT components can work together to support reflective 875 telemetry, as shown in Figure 8. 877 +---------------------+ 878 | | 879 +------+ Applications |<------+ 880 | | | | 881 | +---------------------+ | 882 | Technique Selection | 883 | and Integration | 884 | | 885 |Flexible Flexible | 886 |Flow, reflection-loop Data | 887 |Packet, Export| 888 |and Data | 889 |Selection +----+----+ 890 V +---------+| 891 +----------+ Encapsulation +---------+|| 892 | Head | and Tunneling | Path ||| 893 | Node |----------------------->| Nodes ||+ 894 | | | |+ 895 +----------+ +---------+ 896 DNP DNP 898 Figure 8: IFIT-based Reflective Telemetry 900 An application may pick a suite of telemetry techniques based on its 901 requirements and apply an initial technique to the data plane. It 902 then configures the head nodes to decide the initial target flows/ 903 packets and telemetry data set, the encapsulation and tunneling 904 scheme based on the underlying network architecture, and the IFIT- 905 capable nodes to decide the initial telemetry data export policy. 906 Based on the network condition and the analysis results of the 907 telemetry data, the application can change the telemetry technique, 908 the flow/data selection policy, and the data export approach in real 909 time without breaking the normal network operation. Many of such 910 dynamic changes can be done through loading and unloading DNPs. 912 The reflective telemetry enabled by the IFIT allows numerous new 913 applications suitable for future network operation architecture. 915 4.1. Example: Intelligent Multipoint Performance Monitoring 917 [I-D.ietf-ippm-multipoint-alt-mark] describes an intelligent 918 performance management based on the network condition. The idea is 919 to split the monitoring network into clusters. The cluster partition 920 that can be applied to every type of network graph and the 921 possibility to combine clusters at different levels enable the so- 922 called Network Zooming. It allows a controller to calibrate the 923 network telemetry, so that it can start without examining in depth 924 and monitor the network as a whole. In case of necessity (packet 925 loss or too high delay), an immediate detailed analysis can be 926 reconfigured. In particular, the controller, that is aware of the 927 network topology, can set up the most suited cluster partition by 928 changing the traffic filter or activate new measurement points and 929 the problem can be localized with a step-by-step process. 931 An application on top of the controllers can manage such mechanism 932 and IFIT's architecture allows its dynamic and reflective operation. 934 4.2. Example: Intent-based Network Monitoring 936 User Intents 937 | 938 V Per-packet 939 +------------+ Telemetry 940 ACL | | Data 941 +--------+ Controller |<--------+ 942 | | | | 943 | +--+---------+ | 944 | | ^ | 945 | |DNPs |Network | 946 | | |Information| 947 | V | | 948 +------+-------------------+-----------+---+ 949 | | | 950 | V +------+ | 951 | +-------+ +------+| | 952 | | Head | IFIT Domain +------+|| | 953 | | Node | |Path ||+ | 954 | | | |Nodes |+ | 955 | +-------+ +------+ | 956 +------------------------------------------+ 958 Figure 9: Intent-based Monitoring 960 In this example, a user can express high level intents for network 961 monitoring. The controller translates an intent and configure the 962 corresponding DNPs in IFIT-capable nodes which collect necessary 963 network information. Based on the real-time information feedback, 964 the controller runs a local algorithm to determine the suspicious 965 flows. It then deploys ACLs to the head node to initiate the high 966 precision per-packet on-path telemetry for these flows. 968 5. Standard Status and Gaps 970 A complete IFIT-based solution needs standard interfaces for 971 configuration and data extraction, and standard encapsulation on 972 various transport protocols. It may also need standard API and 973 primitives for application programming and deployment. The draft 974 [I-D.brockners-opsawg-ioam-deployment] summarizes some current 975 proposals on encapsulation and data export for IOAM. These works 976 should be extended or modified to support other types of on-path 977 telemetry techniques and other transport protocols. The high-level 978 IFIT helps to develop coherent and universal standard encapsulation 979 and data export approaches. 981 5.1. Encapsulation in Transport Protocols 983 Since the introduction of IOAM, the IOAM option header encapsulation 984 schemes in various network protocols have been proposed. Similar 985 encapsulation schemes need to be extended to cover the other on-path 986 telemetry techniques. On the other hand, the encapsulation scheme 987 for some popular protocols, such as MPLS and IPv4, are noticeably 988 missing. It is important to provide the encapsulation schemes for 989 these protocols because they are still prevalent in carrier networks. 990 IFIT needs to provide solutions to apply the on-path flow telemetry 991 techniques in such networks. PBT-M 992 [I-D.song-ippm-postcard-based-telemetry] does not introduce new 993 headers to the packets so the trouble of encapsulation for a new 994 header is avoided. While there are some proposals which allow new 995 header encapsulation in MPLS packets (e.g., 996 [I-D.song-mpls-extension-header]) or in IPv4 packets (e.g., 997 [I-D.herbert-ipv4-eh]), they are still in their infancy stage and 998 require significant future work. For the meantime, in a confined 999 IFIT domain, pre-standard encapsulation approaches may be applied. 1001 5.2. Tunneling Support 1003 In carrier networks, it is common for user traffic to traverse 1004 various tunnels for QoS, traffic engineering, or security. IFIT 1005 supports both the uniform mode and the pipe mode for tunnel support 1006 as described in [I-D.song-ippm-ioam-tunnel-mode]. With such 1007 flexibility, the operator can either gain a true end-to-end 1008 visibility or apply a hierarchical approach which isolates the 1009 monitoring domain between customer and provider. 1011 5.3. Deployment Automation 1013 In addition, standard approaches that automates the function 1014 configuration, and capability query and advertisement, either in a 1015 centralized fashion or a distributed fashion, are still immature. 1017 The draft [I-D.zhou-ippm-ioam-yang] provides the YANG model for IOAM 1018 configuration. Similar models needs to be defined for other 1019 techniques. It is also helpful to provide standards-based approaches 1020 for configuration in various network environments. For example, in 1021 segment routing networks, extensions to BGP or PCEP can be defined to 1022 distribute SR policies carrying IFIT information, so that IFIT 1023 behavior can be enabled automatically when the SR policy is applied. 1024 [I-D.chen-pce-sr-policy-ifit] proposes to extend PCEP policy for IFIT 1025 configuration in segment routing networks. 1026 [I-D.qin-idr-sr-policy-ifit] proposes to extend BGP policy instead 1027 for IFIT configuration in segment routing networks. Additional 1028 capability discovery and dissemination will be needed for other types 1029 of networks. 1031 To realize the potential of IFIT, programming and deploying DNPs are 1032 important. ForCES [RFC5810] is a standard protocol for network 1033 device programming, which can be used for DNP deployment. Currently 1034 some related works such as [I-D.wwx-netmod-event-yang] and 1035 [I-D.bwd-netmod-eca-framework] have proposed to use YANG model to 1036 define the smart policies which can be used to implement DNPs. In 1037 the future, other approaches for hardware and software-based 1038 functions can be development to enhance the programmability and 1039 flexibility. 1041 6. Summary 1043 IFIT is a high-level framework for applying on-path telemetry 1044 techniques, and this document has outlined how the framework may be 1045 used to solve essential use cases. IFIT enables a practical data- 1046 plane telemetry application based on two basic on-path traffic data 1047 collection modes: passport and postcard. 1049 IFIT addresses the key challenges for operators to deploy a complete 1050 on-path telemetry solution. However, as a reference and open 1051 framework, IFIT only describes the basic functions of each identified 1052 component and suggests possible applications. It has no intention of 1053 specifying the implementation of the components and the interfaces 1054 between the components. The compliance of IFIT is by no means 1055 mandatory either. Instead, this informational document aims to 1056 clarify the problem domain, and summarize the best practices and 1057 sensible system design considerations. IFIT can guide the analysis 1058 of the current standard status and gaps, and motivate new works to 1059 complete the ecosystem. IFIT enables data-plane reflective telemetry 1060 applications for advanced network operations. 1062 Having a high-level framework covering a class of related techniques 1063 also promotes a holistic approach for standard development and helps 1064 to avoid duplicated efforts and piecemeal solutions that only focus 1065 on a specific technique while omitting the compatibility and 1066 extensibility issues, which is important to a healthy ecosystem for 1067 network telemetry. 1069 7. Security Considerations 1071 In addition to the specific security issues discussed in each 1072 individual document on on-path telemetry, this document considers the 1073 overall security issues at the IFIT system level. This should serve 1074 as a guide to the on-path telemetry application developers and users. 1076 8. IANA Considerations 1078 This document includes no request to IANA. 1080 9. Contributors 1082 Other major contributors of this document include Giuseppe Fioccola, 1083 Daniel King, Zhenqiang Li, Zhenbin Li, Tianran Zhou, and James 1084 Guichard. 1086 10. Acknowledgments 1088 We thank Diego Lopez, Shwetha Bhandari, Joe Clarke, Adrian Farrel, 1089 Frank Brockners, Al Morton, Alex Clemm, Alan DeKok, and Warren Kumari 1090 for their constructive suggestions for improving this document. 1092 11. References 1094 11.1. Normative References 1096 [RFC2119] Bradner, S., "Key words for use in RFCs to Indicate 1097 Requirement Levels", BCP 14, RFC 2119, 1098 DOI 10.17487/RFC2119, March 1997, 1099 . 1101 [RFC7799] Morton, A., "Active and Passive Metrics and Methods (with 1102 Hybrid Types In-Between)", RFC 7799, DOI 10.17487/RFC7799, 1103 May 2016, . 1105 [RFC8174] Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC 1106 2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174, 1107 May 2017, . 1109 11.2. Informative References 1111 [CMSketch] 1112 Cormode, G. and S. Muthukrishnan, "An improved data stream 1113 summary: the count-min sketch and its applications", 2005, 1114 . 1116 [I-D.brockners-opsawg-ioam-deployment] 1117 Brockners, F., Bhandari, S., and d. 1118 daniel.bernier@bell.ca, "In-situ OAM Deployment", draft- 1119 brockners-opsawg-ioam-deployment-02 (work in progress), 1120 September 2020. 1122 [I-D.bwd-netmod-eca-framework] 1123 Boucadair, M., WU, Q., Wang, Z., King, D., and C. Xie, 1124 "Framework for Use of ECA (Event Condition Action) in 1125 Network Self Management", draft-bwd-netmod-eca- 1126 framework-00 (work in progress), November 2019. 1128 [I-D.chen-pce-sr-policy-ifit] 1129 Chen, H., Yuan, H., Zhou, T., Li, W., Fioccola, G., and Y. 1130 Wang, "PCEP SR Policy Extensions to Enable IFIT", draft- 1131 chen-pce-sr-policy-ifit-02 (work in progress), July 2020. 1133 [I-D.herbert-ipv4-eh] 1134 Herbert, T., "IPv4 Extension Headers and Flow Label", 1135 draft-herbert-ipv4-eh-01 (work in progress), May 2019. 1137 [I-D.ietf-ippm-ioam-data] 1138 Brockners, F., Bhandari, S., and T. Mizrahi, "Data Fields 1139 for In-situ OAM", draft-ietf-ippm-ioam-data-10 (work in 1140 progress), July 2020. 1142 [I-D.ietf-ippm-ioam-direct-export] 1143 Song, H., Gafni, B., Zhou, T., Li, Z., Brockners, F., 1144 Bhandari, S., Sivakolundu, R., and T. Mizrahi, "In-situ 1145 OAM Direct Exporting", draft-ietf-ippm-ioam-direct- 1146 export-01 (work in progress), August 2020. 1148 [I-D.ietf-ippm-multipoint-alt-mark] 1149 Fioccola, G., Cociglio, M., Sapio, A., and R. Sisto, 1150 "Multipoint Alternate Marking method for passive and 1151 hybrid performance monitoring", draft-ietf-ippm- 1152 multipoint-alt-mark-09 (work in progress), March 2020. 1154 [I-D.ietf-opsawg-ntf] 1155 Song, H., Qin, F., Martinez-Julia, P., Ciavaglia, L., and 1156 A. Wang, "Network Telemetry Framework", draft-ietf-opsawg- 1157 ntf-04 (work in progress), September 2020. 1159 [I-D.mirsky-ippm-hybrid-two-step] 1160 Mirsky, G., Lingqiang, W., and G. Zhui, "Hybrid Two-Step 1161 Performance Measurement Method", draft-mirsky-ippm-hybrid- 1162 two-step-05 (work in progress), April 2020. 1164 [I-D.qin-idr-sr-policy-ifit] 1165 Qin, F., Yuan, H., Zhou, T., Fioccola, G., and Y. Wang, 1166 "BGP SR Policy Extensions to Enable IFIT", draft-qin-idr- 1167 sr-policy-ifit-04 (work in progress), October 2020. 1169 [I-D.song-ippm-ioam-tunnel-mode] 1170 Song, H., Li, Z., Zhou, T., and Z. Wang, "In-situ OAM 1171 Processing in Tunnels", draft-song-ippm-ioam-tunnel- 1172 mode-00 (work in progress), June 2018. 1174 [I-D.song-ippm-postcard-based-telemetry] 1175 Song, H., Zhou, T., Li, Z., Shin, J., and K. Lee, 1176 "Postcard-based On-Path Flow Data Telemetry", draft-song- 1177 ippm-postcard-based-telemetry-07 (work in progress), April 1178 2020. 1180 [I-D.song-mpls-extension-header] 1181 Song, H., Li, Z., Zhou, T., and L. Andersson, "MPLS 1182 Extension Header", draft-song-mpls-extension-header-02 1183 (work in progress), February 2019. 1185 [I-D.song-multicast-telemetry] 1186 Song, H., McBride, M., and G. Mirsky, "Requirement and 1187 Solution for Multicast Traffic On-path Telemetry", draft- 1188 song-multicast-telemetry-04 (work in progress), April 1189 2020. 1191 [I-D.wwx-netmod-event-yang] 1192 Bierman, A., WU, Q., Bryskin, I., Birkholz, H., Liu, X., 1193 and B. Claise, "A YANG Data model for ECA Policy 1194 Management", draft-wwx-netmod-event-yang-09 (work in 1195 progress), July 2020. 1197 [I-D.zhou-ippm-enhanced-alternate-marking] 1198 Zhou, T., Fioccola, G., Lee, S., Cociglio, M., and W. Li, 1199 "Enhanced Alternate Marking Method", draft-zhou-ippm- 1200 enhanced-alternate-marking-05 (work in progress), July 1201 2020. 1203 [I-D.zhou-ippm-ioam-yang] 1204 Zhou, T., Guichard, J., Brockners, F., and S. Raghavan, "A 1205 YANG Data Model for In-Situ OAM", draft-zhou-ippm-ioam- 1206 yang-08 (work in progress), July 2020. 1208 [passport-postcard] 1209 Handigol, N., Heller, B., Jeyakumar, V., Mazieres, D., and 1210 N. McKeown, "Where is the debugger for my software-defined 1211 network?", 2012, 1212 . 1214 [RFC5810] Doria, A., Ed., Hadi Salim, J., Ed., Haas, R., Ed., 1215 Khosravi, H., Ed., Wang, W., Ed., Dong, L., Gopal, R., and 1216 J. Halpern, "Forwarding and Control Element Separation 1217 (ForCES) Protocol Specification", RFC 5810, 1218 DOI 10.17487/RFC5810, March 2010, 1219 . 1221 [RFC7011] Claise, B., Ed., Trammell, B., Ed., and P. Aitken, 1222 "Specification of the IP Flow Information Export (IPFIX) 1223 Protocol for the Exchange of Flow Information", STD 77, 1224 RFC 7011, DOI 10.17487/RFC7011, September 2013, 1225 . 1227 11.3. URIs 1229 [1] https://developers.google.com/protocol-buffers/ 1231 Authors' Addresses 1233 Haoyu Song 1234 Futurewei 1235 2330 Central Expressway 1236 Santa Clara 1237 USA 1239 Email: haoyu.song@futurewei.com 1241 Fengwei Qin 1242 China Mobile 1243 No. 32 Xuanwumenxi Ave., Xicheng District 1244 Beijing, 100032 1245 P.R. China 1247 Email: qinfengwei@chinamobile.com 1248 Huanan Chen 1249 China Telecom 1250 P. R. China 1252 Email: chenhuan6@chinatelecom.cn 1254 Jaehwan Jin 1255 LG U+ 1256 South Korea 1258 Email: daenamu1@lguplus.co.kr 1260 Jongyoon Shin 1261 SK Telecom 1262 South Korea 1264 Email: jongyoon.shin@sk.com