--- 1/draft-ietf-opsawg-ntf-05.txt 2021-01-21 14:13:10.885785530 -0800 +++ 2/draft-ietf-opsawg-ntf-06.txt 2021-01-21 14:13:10.965787568 -0800 @@ -1,167 +1,185 @@ OPSAWG H. Song Internet-Draft Futurewei Intended status: Informational F. Qin -Expires: April 12, 2021 China Mobile +Expires: July 25, 2021 China Mobile P. Martinez-Julia NICT L. Ciavaglia Nokia A. Wang China Telecom - October 9, 2020 + January 21, 2021 Network Telemetry Framework - draft-ietf-opsawg-ntf-05 + draft-ietf-opsawg-ntf-06 Abstract - Network telemetry is the technology for gaining network insight and - facilitating efficient and automated network management. It engages - various techniques for remote data collection, correlation, and - consumption. This document provides an architectural framework for - network telemetry, motivated by the network operation challenges and - requirements. As evidenced by some key characteristics and industry - practices, network telemetry covers technologies and protocols beyond - the conventional network Operations, Administration, and Management - (OAM). It promises better flexibility, scalability, accuracy, - coverage, and performance and allows automated control loops to suit - both today's and tomorrow's network operation. This document - clarifies the terminologies and classifies the modules and components - of a network telemetry system from several different perspectives. - The framework and taxonomy help to set a common ground for the - collection of related work and provide guidance for related technique - and standard developments. + Network telemetry is a technology for gaining network insight and + facilitating efficient and automated network management. It + encompasses various techniques for remote data generation, + collection, correlation, and consumption. This document describes an + architectural framework for network telemetry, motivated by + challenges that are encountered as part of the operation of networks + and by the requirements that ensue. Network telemetry, as + necessitated by best industry practices, covers technologies and + protocols that extend beyond conventional network Operations, + Administration, and Management (OAM). The presented network + telemetry framework promises better flexibility, scalability, + accuracy, coverage, and performance. In addition, it facilitates the + implementation of automated control loops to address both today's and + tomorrow's network operational needs. This document clarifies the + terminologies and classifies the modules and components of a network + telemetry system from several different perspectives. The framework + and taxonomy help to set a common ground for the collection of + related work and provide guidance for related technique and standard + developments. Status of This Memo This Internet-Draft is submitted in full conformance with the provisions of BCP 78 and BCP 79. Internet-Drafts are working documents of the Internet Engineering Task Force (IETF). Note that other groups may also distribute working documents as Internet-Drafts. The list of current Internet- Drafts is at https://datatracker.ietf.org/drafts/current/. Internet-Drafts are draft documents valid for a maximum of six months and may be updated, replaced, or obsoleted by other documents at any time. It is inappropriate to use Internet-Drafts as reference material or to cite them other than as "work in progress." - This Internet-Draft will expire on April 12, 2021. + This Internet-Draft will expire on July 25, 2021. Copyright Notice - Copyright (c) 2020 IETF Trust and the persons identified as the + Copyright (c) 2021 IETF Trust and the persons identified as the document authors. All rights reserved. This document is subject to BCP 78 and the IETF Trust's Legal Provisions Relating to IETF Documents (https://trustee.ietf.org/license-info) in effect on the date of publication of this document. Please review these documents carefully, as they describe your rights and restrictions with respect to this document. Code Components extracted from this document must include Simplified BSD License text as described in Section 4.e of the Trust Legal Provisions and are provided without warranty as described in the Simplified BSD License. Table of Contents 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3 2. Background . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.1. Telemetry Data Coverage . . . . . . . . . . . . . . . . . 5 2.2. Use Cases . . . . . . . . . . . . . . . . . . . . . . . . 5 - 2.3. Challenges . . . . . . . . . . . . . . . . . . . . . . . 6 + 2.3. Challenges . . . . . . . . . . . . . . . . . . . . . . . 7 2.4. Glossary . . . . . . . . . . . . . . . . . . . . . . . . 8 - 2.5. Network Telemetry . . . . . . . . . . . . . . . . . . . . 9 - 3. The Necessity of a Network Telemetry Framework . . . . . . . 11 + 2.5. Network Telemetry . . . . . . . . . . . . . . . . . . . . 10 + 3. The Necessity of a Network Telemetry Framework . . . . . . . 12 4. Network Telemetry Framework . . . . . . . . . . . . . . . . . 13 4.1. Top Level Modules . . . . . . . . . . . . . . . . . . . . 13 - 4.1.1. Management Plane Telemetry . . . . . . . . . . . . . 16 - 4.1.2. Control Plane Telemetry . . . . . . . . . . . . . . . 16 - 4.1.3. Data Plane Telemetry . . . . . . . . . . . . . . . . 17 - 4.1.4. External Data Telemetry . . . . . . . . . . . . . . . 19 - 4.2. Second Level Function Components . . . . . . . . . . . . 19 - 4.3. Data Acquiring Mechanism and Type Abstraction . . . . . . 21 - 4.4. Existing Works Mapped in the Framework . . . . . . . . . 23 - 5. Evolution of Network Telemetry . . . . . . . . . . . . . . . 24 - 6. Security Considerations . . . . . . . . . . . . . . . . . . . 25 - 7. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 26 - 8. Contributors . . . . . . . . . . . . . . . . . . . . . . . . 26 - 9. Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . 26 - 10. Informative References . . . . . . . . . . . . . . . . . . . 26 - Appendix A. A Survey on Existing Network Telemetry Techniques . 30 - A.1. Management Plane Telemetry . . . . . . . . . . . . . . . 30 - A.1.1. Push Extensions for NETCONF . . . . . . . . . . . . . 30 - A.1.2. gRPC Network Management Interface . . . . . . . . . . 31 - A.2. Control Plane Telemetry . . . . . . . . . . . . . . . . . 31 - A.2.1. BGP Monitoring Protocol . . . . . . . . . . . . . . . 31 - A.3. Data Plane Telemetry . . . . . . . . . . . . . . . . . . 32 - A.3.1. The Alternate Marking technology . . . . . . . . . . 32 - A.3.2. Dynamic Network Probe . . . . . . . . . . . . . . . . 33 - A.3.3. IP Flow Information Export (IPFIX) protocol . . . . . 33 - A.3.4. In-Situ OAM . . . . . . . . . . . . . . . . . . . . . 34 - A.3.5. Postcard Based Telemetry . . . . . . . . . . . . . . 34 - A.4. External Data and Event Telemetry . . . . . . . . . . . . 34 - A.4.1. Sources of External Events . . . . . . . . . . . . . 34 - A.4.2. Connectors and Interfaces . . . . . . . . . . . . . . 36 - Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 36 + 4.1.1. Management Plane Telemetry . . . . . . . . . . . . . 17 + 4.1.2. Control Plane Telemetry . . . . . . . . . . . . . . . 17 + 4.1.3. Forwarding Plane Telemetry . . . . . . . . . . . . . 18 + 4.1.4. External Data Telemetry . . . . . . . . . . . . . . . 20 + 4.2. Second Level Function Components . . . . . . . . . . . . 20 + 4.3. Data Acquiring Mechanism and Type Abstraction . . . . . . 22 + 4.4. Existing Works Mapped in the Framework . . . . . . . . . 24 + 5. Evolution of Network Telemetry . . . . . . . . . . . . . . . 26 + 6. Security Considerations . . . . . . . . . . . . . . . . . . . 26 + 7. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 27 + 8. Contributors . . . . . . . . . . . . . . . . . . . . . . . . 28 + 9. Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . 28 + 10. Informative References . . . . . . . . . . . . . . . . . . . 28 + Appendix A. A Survey on Existing Network Telemetry Techniques . 32 + A.1. Management Plane Telemetry . . . . . . . . . . . . . . . 32 + A.1.1. Push Extensions for NETCONF . . . . . . . . . . . . . 32 + A.1.2. gRPC Network Management Interface . . . . . . . . . . 33 + A.2. Control Plane Telemetry . . . . . . . . . . . . . . . . . 33 + A.2.1. BGP Monitoring Protocol . . . . . . . . . . . . . . . 33 + A.3. Data Plane Telemetry . . . . . . . . . . . . . . . . . . 34 + A.3.1. The Alternate Marking technology . . . . . . . . . . 34 + A.3.2. Dynamic Network Probe . . . . . . . . . . . . . . . . 35 + A.3.3. IP Flow Information Export (IPFIX) protocol . . . . . 35 + A.3.4. In-Situ OAM . . . . . . . . . . . . . . . . . . . . . 35 + A.3.5. Postcard Based Telemetry . . . . . . . . . . . . . . 36 + A.4. External Data and Event Telemetry . . . . . . . . . . . . 36 + A.4.1. Sources of External Events . . . . . . . . . . . . . 36 + A.4.2. Connectors and Interfaces . . . . . . . . . . . . . . 37 + Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 38 1. Introduction Network visibility is the ability of management tools to see the - state and behavior of a network. It is essential for successful - network operation. Network telemetry is the process of measuring, - correlating, recording, and distributing information about the - behavior of a network. Network telemetry has been considered as an - ideal means to gain sufficient network visibility with better - flexibility, scalability, accuracy, coverage, and performance than - some conventional network Operations, Administration, and Management - (OAM) techniques. + state and behavior of a network, which is essential for successful + network operation. Network Telemetry revolves around network data + that can help provide insights about the current state of the + network, including network devices, forwarding, control, and + management planes, and that can be generated and obtained through a + variety of techniques, including but not limited to network + instrumentation and measurements, and that can be processed for + purposes ranging from service assurance to network security using a + wide variety of techniques including machine learning, data analysis, + and correlation. In this document, Network Telemetry refer to both + the data itself (i.e., "Network Telemetry Data"), and the techniques + and processes used to generate, export, collect, and consume that + data for use by potentially automated management applications. + Network telemetry extends beyond the conventional network Operations, + Administration, and Management (OAM) techniques and expects to + support better flexibility, scalability, accuracy, coverage, and + performance. However, the term of network telemetry lacks a solid and unambiguous definition. The scope and coverage of it cause confusion and misunderstandings. It is beneficial to clarify the concept and provide a clear architectural framework for network telemetry, so we can articulate the technical field, and better align the related techniques and standard works. To fulfill such an undertaking, we first discuss some key characteristics of network telemetry which set a clear distinction from the conventional network OAM and show that some conventional OAM technologies can be considered a subset of the network telemetry technologies. We then provide an architectural framework for network - telemetry by partitioning a network telemetry system into four - modules each with the same building components and data abstracts. - We show how the network telemetry framework can benefit the current - and future network operations. Based on the distinction of modules - and function components, we can map the existing and emerging - techniques and protocols into the framework. The framework can also - simplify the tasks for designing, maintaining, and understanding a - network telemetry system. At last, we outline the evolution stages - of the network telemetry system and discuss the potential security - concerns. + telemetry which includes four modules, each concerned with a + different category of telemetry data and corresponding procedures. + All the modules are internally structured in the same way, including + components that allow to configure data sources with regards to what + data to generate and how to make that available to client + applications, components that instrument the underlying data sources, + and components that perform the actual rendering, encoding, and + exporting of the generated data. We show how the network telemetry + framework can benefit the current and future network operations. + Based on the distinction of modules and function components, we can + map the existing and emerging techniques and protocols into the + framework. The framework can also simplify the tasks for designing, + maintaining, and understanding a network telemetry system. At last, + we outline the evolution stages of the network telemetry system and + discuss the potential security concerns. The purpose of the framework and taxonomy is to set a common ground for the collection of related work and provide guidance for future technique and standard developments. To the best of our knowledge, this document is the first such effort for network telemetry in industry standards organizations. 2. Background The term "big data" is used to describe the extremely large volume of data sets that can be analyzed computationally to reveal patterns, - trends, and associations. Network is undoubtedly a source of big - data because of its scale and all the traffic goes through it. It is - easy to see that network OAM can benefit from network big data. + trends, and associations. Networks are undoubtedly a source of big + data because of their scale and the volume of network traffic they + forward. It is easy to see that network operations can benefit from + network big data. Today one can access advanced big data analytics capability through a plethora of commercial and open source platforms (e.g., Apache Hadoop), tools (e.g., Apache Spark), and techniques (e.g., machine learning). Thanks to the advance of computing and storage technologies, network big data analytics gives network operators an opportunity to gain network insights and move towards network autonomy. Some operators start to explore the application of Artificial Intelligence (AI) to make sense of network data. Software tools can use the network data to detect and react on network faults, @@ -180,57 +198,58 @@ However, while the data processing capability is improved and applications are hungry for more data, the networks lag behind in extracting and translating network data into useful and actionable information in efficient ways. The system bottleneck is shifting from data consumption to data supply. Both the number of network nodes and the traffic bandwidth keep increasing at a fast pace. The network configuration and policy change at smaller time slots than before. More subtle events and fine-grained data through all network planes need to be captured and exported in real time. In a nutshell, - it is a challenge to get enough high-quality data out of network - efficiently, timely, and flexibly. Therefore, we need to examine the - existing network technologies and protocols, and identify any - potential technique and standard gaps based on the real network and - device architectures. + it is a challenge to get enough high-quality data out of the network + in a manner that is efficient, timely, and flexible. Therefore, we + need to survey the existing technologies and protocols and identify + any potential gaps. - In the remaining of this section, first we clarify the scope of + In the remainder of this section, first we clarify the scope of network data (i.e., telemetry data) concerned in the context. Then, we discuss several key use cases for today's and future network operations. Next, we show why the current network OAM techniques and protocols are insufficient for these use cases. The discussion underlines the need of new methods, techniques, and protocols which - we assign under an umbrella term - network telemetry. + we assign under the umbrella term - Network Telemetry. 2.1. Telemetry Data Coverage Any information that can be extracted from networks (including data plane, control plane, and management plane) and used to gain visibility or as basis for actions is considered telemetry data. It includes statistics, event records and logs, snapshots of state, configuration data, etc. It also covers the outputs of any active and passive measurements [RFC7799]. Specially, raw data can be - processed in network before sending to a data consumer. Such - processed data are also telemetry data in the context. A - classification of the telemetry data form is provided in Section 4. + processed in-network before being sent to a data consumer. Such + processed data is also considered telemetry data. A classification + of telemetry data is provided in Section 4. 2.2. Use Cases - These use cases are essential for network operations. While the list - is by no means exhaustive, it is enough to highlight the requirements - for data velocity, variety, volume, and veracity in networks. + The following set of use cases is essential for network operations. + While the list is by no means exhaustive, it is enough to highlight + the requirements for data velocity, variety, volume, and veracity in + networks. - Security: Network intrusion detection and prevention need monitor - network traffic and activities, and act upon anomalies. Given the - more and more sophisticated attack vector and higher and higher - tolls due to security breach, new tools and techniques need to be - developed, relying on wider and deeper visibility in networks. + Security: Network intrusion detection and prevention systems need to + monitor network traffic and activities and act upon anomalies. + Given increasingly sophisticated attack vector coupled with + increasingly severe consequences of security breaches, new tools + and techniques need to be developed, relying on wider and deeper + visibility in networks. Policy and Intent Compliance: Network policies are the rules that constraint the services for network access, provide service differentiation, or enforce specific treatment on the traffic. For example, a service function chain is a policy that requires the selected flows to pass through a set of ordered network functions. Intent, as defined in [I-D.irtf-nmrg-ibn-concepts-definitions], is a set of operational goal that a network should meet and outcomes that a network is supposed to deliver, defined in a declarative manner without @@ -241,59 +260,59 @@ needs to be reported immediately. SLA Compliance: A Service-Level Agreement (SLA) defines the level of service a user expects from a network operator, which include the metrics for the service measurement and remedy/penalty procedures when the service level misses the agreement. Users need to check if they get the service as promised and network operators need to evaluate how they can deliver the services that can meet the SLA based on realtime network measurement. - Root Cause Analysis: Any network failure can be the cause or effect - of a sequence of chained events. Troubleshooting and recovery - require quick identification of the root cause of any observable - issues. However, the root cause is not always straightforward to - identify, especially when the failure is sporadic and the related - and unrelated events are overwhelming and interleaved. While - machine learning technologies can be used for root cause analysis, - it up to the network to sense and provide the relevant data. + Root Cause Analysis: Any network failure can be the effect of a + sequence of chained events. Troubleshooting and recovery require + quick identification of the root cause of any observable issues. + However, the root cause is not always straightforward to identify, + especially when the failure is sporadic and the number of event + messages, both related and unrelated to the same cause, is + overwhelming. While machine learning technologies can be used for + root cause analysis, it up to the network to sense and provide the + relevant data. Network Optimization: This covers all short-term and long-term network optimization techniques, including load balancing, Traffic Engineering (TE), and network planning. Network operators are motivated to optimize their network utilization and differentiate services for better Return On Investment (ROI) or lower Capital Expenditures (CAPEX). The first step is to know the real-time network conditions before applying policies for traffic manipulation. In some cases, micro-bursts need to be detected in a very short time-frame so that fine-grained traffic control can - be applied to avoid network congestion. The long-term network - capacity planning and topology augmentation rely on the - accumulated data of network operations. + be applied to avoid network congestion. Long-term planning of + network capacity and topology requires analysis of real-world + network telemetry data that is obtained over long periods of time. Event Tracking and Prediction: The visibility of traffic path and performance is critical for services and applications that rely on healthy network operation. Numerous related network events are of interest to network operators. For example, Network operators want to learn where and why packets are dropped for an application flow. They also want to be warned of issues in advance so proactive actions can be taken to avoid catastrophic consequences. 2.3. Challenges For a long time, network operators have relied upon SNMP [RFC3416], Command-Line Interface (CLI), or Syslog to monitor the network. Some other OAM techniques as described in [RFC7276] are also used to - facilitate network troubleshooting. these conventional techniques + facilitate network troubleshooting. These conventional techniques are not sufficient to support the above use cases for the following - reasons, which explains why new standards and techniques keep - emerging and the needs remain high: + reasons: o Most use cases need to continuously monitor the network and dynamically refine the data collection in real-time. The poll- based low-frequency data collection is ill-suited for these applications. Subscription-based streaming data directly pushed from the data source (e.g., the forwarding chip) is preferred to provide enough data quantity and precision at scale. o Comprehensive data is needed from packet processing engine to traffic manager, from line cards to main control board, from user @@ -328,27 +347,36 @@ precision which are beyond the capability of the existing techniques. o The conventional passive measurement techniques can either consume excessive network resources and render excessive redundant data, or lead to inaccurate results; on the other hand, the conventional active measurement techniques can interfere with the user traffic and their results are indirect. Techniques that can collect direct and on-demand data from user traffic are more favorable. + These challenges were addressed by newer standards and techniques + (e.g., IPFIX/Netflow, PSAMP, IOAM, and YANG-Push) and more are + emerging. These standards and techniques need to be recognized and + accommodated in a new framework. + 2.4. Glossary Before further discussion, we list some key terminology and acronyms used in this documents. We make an intended differentiation between - network telemetry and network OAM. However, it should be understood - that there is not a hard-line distinction between the two concepts. - Rather, some OAM techniques are in the scope of network telemetry. + the terms of network telemetry and OAM. However, it should be + understood that there is not a hard-line distinction between the two + concepts. Rather, network telemetry is considered as the extension + of OAM. It covers all the existing OAM protocols but puts more + emphasis on the newer and emerging techniques and protocols + concerning all aspects of network data from acquisition to + consumption. AI: Artificial Intelligence. In network domain, AI refers to the machine-learning based technologies for automated network operation and other tasks. AM: Alternate Marking, a flow performance measurement method, specified in [RFC8321]. BMP: BGP Monitoring Protocol, specified in [RFC7854]. @@ -367,31 +395,30 @@ IPFIX: IP Flow Information Export Protocol, specified in [RFC7011]. IOAM: In-situ OAM, a dataplane on-path telemetry technique. NETCONF: Network Configuration Protocol, specified in [RFC6241]. NetFlow: A Cisco protocol for flow record collecting, described in [RFC3594]. - Network Telemetry: Acquiring and processing network data remotely - for network monitoring and operation. A general term for a large - set of network visibility techniques and protocols, with the - characteristics defined in this document. Network telemetry + Network Telemetry: The process and instrumentation for acquiring and + utilizing network data remotely for network monitoring and + operation. A general term for a large set of network visibility + techniques and protocols, concerning aspects like data generation, + collection, correlation, and consumption. Network telemetry addresses the current network operation issues and enables smooth evolution toward future intent-driven autonomous networks. NMS: Network Management System, referring to applications that allow - network administrators manage a network's software and hardware - components. It usually records data from a network's remote - points to carry out central reporting to a system administrator. + network administrators manage a network. OAM: Operations, Administration, and Maintenance. A group of network management functions that provide network fault indication, fault localization, performance information, and data and diagnosis functions. Most conventional network monitoring techniques and protocols belong to network OAM. PBT: Postcard-Based Telemetry, a dataplane on-path telemetry technique. @@ -399,73 +426,69 @@ [RFC2578]. SNMP: Simple Network Management Protocol. Version 1 and 2 are specified in [RFC1157] and [RFC3416], respectively. YANG: The abbreviation of "Yet Another Next Generation". YANG is a data modeling language for the definition of data sent over network management protocols such as the NETCONF and RESTCONF. YANG is defined in [RFC6020]. - YANG ECN A YANG model for Event-Condition-Action policies, defined + YANG ECA A YANG model for Event-Condition-Action policies, defined in [I-D.wwx-netmod-event-yang]. YANG FSM: A YANG model that describes events, operations, and finite state machine of YANG-defined network elements. YANG PUSH: A method to subscribe pushed data from remote YANG datastore on network devices. Details are specified in [RFC8641] and [RFC8639]. 2.5. Network Telemetry Network telemetry has emerged as a mainstream technical term to refer - to the newer data collection and consumption techniques, - distinguishing itself in some notable ways from the convention - network OAM. Several such techniques have been widely deployed. The - representative techniques and protocols include IPFIX [RFC7011] and - gPRC [grpc]. Network telemetry allows separate entities to acquire - data from network devices so that data can be visualized and analyzed - to support network monitoring and operation. Network telemetry - overlaps with the conventional network OAM and has a wider scope than - it. It is expected that network telemetry can provide the necessary - network insight for autonomous networks and address the shortcomings - of conventional OAM techniques. + to the network data collection and consumption techniques. Several + network telemetry techniques and protocols (e.g., IPFIX [RFC7011] and + gPRC [grpc]) have been widely deployed. Network telemetry allows + separate entities to acquire data from network devices so that data + can be visualized and analyzed to support network monitoring and + operation. Network telemetry covers the conventional network OAM and + has a wider scope. It is expected that network telemetry can provide + the necessary network insight for autonomous networks and address the + shortcomings of conventional OAM techniques. - One difference between the network telemetry and the conventional - network OAM is that in general the network telemetry assumes machines - as data consumer rather than human operators. Hence, the network - telemetry can directly trigger the automated network operation, while - the conventional OAM tools usually help human operators to monitor - and diagnose the networks and guide manual network operations. The - difference leads to very different techniques. + Network telemetry usually assumes machines as data consumer rather + than human operators. Hence, the network telemetry can directly + trigger the automated network operation, while in contrast some + conventional OAM tools are designed and used to help human operators + to monitor and diagnose the networks and guide manual network + operations. Such a proposition leads to very different techniques. - Although the network telemetry techniques are just emerging and - subject to continuous evolution, several characteristics of network - telemetry have been well accepted. Note that network telemetry is - intended to be an umbrella term covering a wide spectrum of - techniques, so the following characteristics are not expected to be - held by every specific technique. + Although new network telemetry techniques are emerging and subject to + continuous evolution, several characteristics of network telemetry + have been well accepted. Note that network telemetry is intended to + be an umbrella term covering a wide spectrum of techniques, so the + following characteristics are not expected to be held by every + specific technique. o Push and Streaming: Instead of polling data from network devices, - the telemetry collector subscribes to the streaming data pushed - from data sources in network devices. + telemetry collectors subscribe to streaming data pushed from data + sources in network devices. o Volume and Velocity: The telemetry data is intended to be consumed by machines rather than by human being. Therefore, the data volume is huge and the processing is often in realtime. o Normalization and Unification: Telemetry aims to address the - overall network automation needs. The piecemeal solutions offered - by the conventional OAM approach are no longer suitable. Efforts - need to be made to normalize the data representation and unify the - protocols. + overall network automation needs. Efforts are made to normalize + the data representation and unify the protocols, so to simplify + data analysis and tying it all in with automation solutions o Model-based: The telemetry data is modeled in advance which allows applications to configure and consume data with ease. o Data Fusion: The data for a single application can come from multiple data sources (e.g., cross-domain, cross-device, and cross-layer) and needs to be correlated to take effect. o Dynamic and Interactive: Since the network telemetry means to be used in a closed control loop for network automation, it needs to @@ -500,87 +523,95 @@ data collection approaches, the new hybrid approach allows to directly collect data for any target flow on its entire forwarding path [I-D.song-opsawg-ifit-framework]. It is worth noting that, a network telemetry system should not be intrusive to normal network operations, by avoiding the pitfall of the "observer effect". That is, it should not change the network behavior and affect the forwarding performance. Otherwise, the whole purpose of network telemetry is defied. - Although in many cases a network telemetry system involves a remote - data collecting, processing, and reacting entity, it is important to - understand that network telemetry does not infer the necessity of - such an entity. Telemetry data producers and consumers can work in - distributed or peer-to-peer fashions instead. In such cases, a - network node can be the direct consumer of telemetry data from other - nodes. + Although in many cases a system for network telemetry involves a + remote data collecting and consuming entity, it is important to + understand that there are no inherent assumptions about how a system + should be architected. Telemetry data producers and consumers can + work in distributed or peer-to-peer fashions rather than assuming a + centralized data consuming entity. In such cases, a network node can + be the direct consumer of telemetry data from other nodes. 3. The Necessity of a Network Telemetry Framework Network data analytics and machine-learning technologies are applied for network operation automation, relying on abundant and coherent - data from networks. The single-sourced and static data acquisition - cannot meet the data requirements. The scattered standards and - diverse techniques are hard to be integrated. It is desirable to - have a framework that classifies and organizes different telemetry - data source and types, defines different components of a network - telemetry system and their interactions, and helps coordinate and - integrate multiple telemetry approaches from different layers. This - allows flexible combinations for different applications, while - normalizing and simplifying interfaces. In detail, such a framework - would benefit application development for the following reasons: + data from networks. Data acquisition that is limited to a single + source and static in nature will in many cases not be sufficient to + meet an application's telemetry data needs. As a result, multiple + data sources, involving a variety of techniques and standards, will + need to be integrated. It is desirable to have a framework that + classifies and organizes different telemetry data source and types, + defines different components of a network telemetry system and their + interactions, and helps coordinate and integrate multiple telemetry + approaches across layers. This allows flexible combinations of data + for different applications, while normalizing and simplifying + interfaces. In detail, such a framework would benefit application + development for the following reasons: - o The future autonomous networks will require a holistic view on - network visibility. All the use cases and applications need to be - supported uniformly and coherently under a single intelligent - agent. Therefore, the protocols and mechanisms should be - consolidated into a minimum yet comprehensive set. A telemetry - framework can help to normalize the technique developments. + o Future networks, autonomous or otherwise, depend on holistic and + comprehensive network visibility. All the use cases and + applications are better to be supported uniformly and coherently + under a single intelligent agent. Therefore, the protocols and + mechanisms should be consolidated into a minimum yet comprehensive + set. A telemetry framework can help to normalize the technique + developments. o Network visibility presents multiple viewpoints. For example, the device viewpoint takes the network infrastructure as the monitoring object from which the network topology and device status can be acquired; the traffic viewpoint takes the flows or packets as the monitoring object from which the traffic quality and path can be acquired. An application may need to switch its viewpoint during operation. It may also need to correlate a service and its impact on network experience to acquire the comprehensive information. o Applications require network telemetry to be elastic in order to - efficiently use the network resource and reduce the performance - impact. Routine network monitoring covers the entire network with - low data sampling rate. When issues arise or trends emerge, the - telemetry data source can be modified and the data rate can be - boosted. + make efficient use of network resources and reduce the impact of + processing related to network telemetry on network performance. + For example, routine network monitoring should cover the entire + network with a low data sampling rate. Only when issues arise or + critical trends emerge should telemetry data source be modified + and telemetry data rates boosted as needed. o Efficient data fusion is critical for applications to reduce the overall quantity of data and improve the accuracy of analysis. A telemetry framework collects together all of the telemetry-related works from different sources and working groups within IETF. This makes it possible to assemble a comprehensive network telemetry system and to avoid repetitious or redundant work. The framework should cover the concepts and components from the standardization - perspective. This document clarifies the layered modules on which - the telemetry is exerted and decomposes the telemetry system into a - set of distinct components that the existing and future work can - easily map to. + perspective. This document describes the modules which make up a + network telemetry framework and decomposes the telemetry system into + a set of distinct components that existing and future work can easily + map to. 4. Network Telemetry Framework The top level network telemetry framework partitions the network telemetry into four modules based on the telemetry data object source - and represents their relationship. The next level framework reveals - that each module replicates the same architecture comprising the same - set of components. Throughout the framework, the same set of + and represents their relationship. At the next level, the framework + decomposes each module into separate components. Each of the modules + follows the same underlying structure, with one component dedicated + to the configuration of data subscriptions and data sources, a second + component dedicated to encoding and exporting data, and a third + component instrumenting the generation of telemetry related to the + underlying resources. Throughout the framework, the same set of abstract data acquiring mechanisms and data types are applied. The two-level architecture with the uniform data abstraction helps accurately pinpoint a protocol or technique to its position in a network telemetry system or disaggregate a network telemetry system into manageable parts. 4.1. Top Level Modules Telemetry can be applied on the forwarding plane, the control plane, and the management plane in a network, as well as other sources out @@ -613,40 +644,53 @@ Figure 1: Modules in Layer Category of NTF The rationale of this partition lies in the different telemetry data objects which result in different data source and export locations. Such differences have profound implications on in-network data programming and processing capability, data encoding and transport protocol, and data bandwidth and latency. We summarize the major differences of the four modules in the - following table. They are compared from six aspects: data object, - data export location, data model, data encoding, telemetry protocol, - and transport method. Data object is the target and source of each - module. Because the data source varies, the data export location - varies. For example, the forwarding plane data are mainly from the - fast path(e.g., forwarding chips) while the control plane data are - mainly from the slow path (e.g., main control CPU). For convenience - and efficiency, it is preferred to export the data from locations - near the source. Because each data export location has different - capability, the proper data model, encoding, and transport method - cannot be kept the same. For example, the forwarding chip has high - throughput but limited capacity for processing complex data and - maintaining states, while the main control CPU is capable of complex - data and state processing, but has limited bandwidth for high - throughput data. As a result, the suitable telemetry protocol for - each module can be different. Some representative techniques are - shown in the corresponding table blocks to highlight the technical - diversity of these modules. The key point is that one cannot expect - to use a universal protocol to cover all the network telemetry - requirements. + following table. They are compared from six aspects: + + o Data Object + + o Data Export Location + + o Data Model + + o Data Encoding + + o Telemetry Protocol + + o Transport Method + + Data object is the target and source of each module. Because the + data source varies, the data export location varies. For example, + the forwarding plane data are mainly from the fast path(e.g., + forwarding chips) while the control plane data are mainly from the + slow path (e.g., main control CPU). For convenience and efficiency, + it is preferred to export the data from locations near the source. + Because each data export location has different capability, the + proper data model, encoding, and transport method cannot be kept the + same. For example, the forwarding chip has high throughput but + limited capacity for processing complex data and maintaining states, + while the main control CPU is capable of complex data and state + processing, but has limited bandwidth for high throughput data. As a + result, the suitable telemetry protocol for each module can be + different. Some representative techniques are shown in the + corresponding table blocks to highlight the technical diversity of + these modules. Note that the selected techniques just reflect the + de-facto state of the art and are not exhaustive. The key point is + that one cannot expect to use a universal protocol to cover all the + network telemetry requirements. +---------+--------------+--------------+--------------+-----------+ | Module | Control | Management | Forwarding | External | | | Plane | Plane | Plane | Data | +---------+--------------+--------------+--------------+-----------+ |Object | control | config. & | flow & packet| terminal, | | | protocol & | operation | QoS, traffic | social & | | | signaling, | state, MIB | stat., buffer| environ- | | | RIB, ACL | | & queue stat.| mental | +---------+--------------+--------------+--------------+-----------+ @@ -686,43 +730,41 @@ the control plane telemetry. The requirements and challenges for each module are summarized as follows. 4.1.1. Management Plane Telemetry The management plane of network elements interacts with the Network Management System (NMS), and provides information such as performance data, network logging data, network warning and defects data, and - network statistics and state data. Some legacy protocols, such as - SNMP and Syslog, are widely used for the management plane. However, - these protocols are insufficient to meet the requirements of the - future automated network operation applications. - - New management plane telemetry protocols should consider the - following requirements: + network statistics and state data. The management plane includes + many protocols, including some that are considered "legacy", such as + SNMP and syslog. Regardless the protocol, management plane telemetry + must address the following requirements: Convenient Data Subscription: An application should have the freedom to choose the data export means such as the data types and the export frequency. Structured Data: For automatic network operation, machines will replace human for network data comprehension. The schema languages such as YANG can efficiently describe structured data and normalize data encoding and transformation. - High Speed Data Transport: In order to retain the information, a - server needs to send a large amount of data at high frequency. - Compact encoding formats are needed to compress the data and - improve the data transport efficiency. The subscription mode, by - replacing the query mode, reduces the interactions between clients - and servers and helps to improve the server's efficiency. + High Speed Data Transport: In order to keep up with the velocity of + information, a server needs to be able to send large amounts of + data at high frequency. Compact encoding formats are needed to + compress the data and improve the data transport efficiency. The + subscription mode, by replacing the query mode, reduces the + interactions between clients and servers and helps to improve the + server's efficiency. 4.1.2. Control Plane Telemetry The control plane telemetry refers to the health condition monitoring of different network control protocols covering Layer 2 to Layer 7. Keeping track of the running status of these protocols is beneficial for detecting, localizing, and even predicting various network issues, as well as network optimization, in real-time and in fine granularity. @@ -745,96 +787,97 @@ and network optimization. An example of the control plane telemetry is the BGP monitoring protocol (BMP), it is currently used to monitoring the BGP routes and enables rich applications, such as BGP peer analysis, AS analysis, prefix analysis, security analysis, and so on. However, the monitoring of other layers, protocols and the cross-layer, cross- protocol KPI correlations are still in their infancy (e.g., the IGP monitoring is missing), which require further research. -4.1.3. Data Plane Telemetry +4.1.3. Forwarding Plane Telemetry - An effective data plane telemetry system relies on the data that the - network device can expose. The data's quality, quantity, and - timeliness must meet some stringent requirements. This raises some - challenges to the network data plane devices where the first hand - data originate. + An effective forwarding plane telemetry system relies on the data + that the network device can expose. The quality, quantity, and + timeliness of data must meet some stringent requirements. This + raises some challenges to the network data plane devices where the + first hand data originate. o A data plane device's main function is user traffic processing and forwarding. While supporting network visibility is important, the telemetry is just an auxiliary function, and it should not impede normal traffic processing and forwarding (i.e., the performance is not lowered and the behavior is not altered due to the telemetry functions). - o The network operation applications requires end-to-end visibility - from various sources, which results in a huge volume of data. - However, the sheer data quantity should not stress the network - bandwidth, regardless of the data delivery approach (i.e., through - in-band or out-of-band channels). + o Network operation applications require end-to-end visibility + across various sources, which can result in a huge volume of data. + However, the sheer data quantity should not exhaust the network + bandwidth, regardless of the data delivery approach (i.e., whether + through in-band or out-of-band channels). o The data plane devices must provide timely data with the minimum possible delay. Long processing, transport, storage, and analysis delay can impact the effectiveness of the control loop and even render the data useless. o The data should be structured and labeled, and easy for applications to parse and consume. At the same time, the data types needed by applications can vary significantly. The data plane devices need to provide enough flexibility and programmability to support the precise data provision for applications. o The data plane telemetry should support incremental deployment and work even though some devices are unaware of the system. This challenge is highly relevant to the standards and legacy networks. - The data plane programmability is essential to support network - telemetry. Newer data plane forwarding chips are equipped with - advanced telemetry features and provide flexibility to support - customized telemetry functions. + Although not specific to the forwarding plane, these challenges are + more difficult to the forwarding plane because of the limited + resource and flexibility. The data plane programmability is + essential to support network telemetry. Newer data plane forwarding + chips are equipped with advanced telemetry features and provide + flexibility to support customized telemetry functions. 4.1.3.1. Technique Taxonomy - There can be multiple possible dimensions to classify the data plane - telemetry techniques. + There can be multiple possible dimensions to classify the forwarding + plane telemetry techniques. - Active, Passive, and Hybrid: The active and passive methods (as well - as the hybrid types) are well documented in [RFC7799]. The - passive methods include TCPDUMP, IPFIX [RFC7011], sflow, and - traffic mirror. These methods usually have low data coverage. - The bandwidth cost is very high in order to improve the data - coverage. On the other hand, the active methods include Ping, - Traceroute, OWAMP [RFC4656], TWAMP [RFC5357], and Cisco's SLA - Protocol [RFC6812]. These methods are intrusive and only provide - indirect network measurement results. The hybrid methods, - including in-situ OAM [I-D.ietf-ippm-ioam-data], IPFPM [RFC8321], - and Multipoint Alternate Marking - [I-D.fioccola-ippm-multipoint-alt-mark], provide a well-balanced - and more flexible approach. However, these methods are also more - complex to implement. + Active, Passive, and Hybrid: Active and passive methods (as well as + the hybrid types) are well documented in [RFC7799]. Passive + methods include TCPDUMP, IPFIX [RFC7011], sflow, and traffic + mirroring. These methods usually have low data coverage. The + bandwidth cost is very high in order to improve the data coverage. + On the other hand, active methods include Ping, OWAMP [RFC4656], + TWAMP [RFC5357], and Cisco's SLA Protocol [RFC6812]. These + methods are intrusive and only provide indirect network + measurement results. Hybrid methods, including in-situ OAM + [I-D.ietf-ippm-ioam-data], IPFPM [RFC8321], and Multipoint + Alternate Marking [I-D.fioccola-ippm-multipoint-alt-mark], provide + a well-balanced and more flexible approach. However, these + methods are also more complex to implement. In-Band and Out-of-Band: The telemetry data, before being exported to some collector, can be carried in user packets. Such methods are considered in-band (e.g., in-situ OAM [I-D.ietf-ippm-ioam-data]). If the telemetry data is directly exported to some collector without modifying the user packets, such methods are considered out-of-band (e.g., postcard-based INT). It is possible to have hybrid methods. For example, only the telemetry instruction or partial data is carried by user packets (e.g., IPFPM [RFC8321]). E2E and In-Network: Some E2E methods start from and end at the network end hosts (e.g., Ping). The other methods work in networks and are transparent to end hosts. However, if needed, - the in-network methods can be easily extended into end hosts. + in-network methods can be easily extended into end hosts. Information Type: Depending on the telemetry objective, the methods can be flow-based (e.g., in-situ OAM [I-D.ietf-ippm-ioam-data]), path-based (e.g., Traceroute), and node-based (e.g., IPFIX [RFC7011]). The various data objects can be packet, flow record, measurement, states, and signal. 4.1.4. External Data Telemetry @@ -875,29 +918,31 @@ possibilities of current and future network systems, as reflected in the incorporation of cognitive capabilities to new hardware and software (virtual) elements. 4.2. Second Level Function Components Reflecting the best current practice, the telemetry module at each plane is further partitioned into five distinct components: Data Query, Analysis, and Storage: This component works at the - application layer. On the one hand, it is responsible for issuing - data requirements. The data of interest can be modeled data - through configuration or custom data through programming. The - data requirements can be queries for one-shot data or + application layer. It is a part of the network management system + at the receiver side. On the one hand, it is responsible for + issuing data requirements. The data of interest can be modeled + data through configuration or custom data through programming. + The data requirements can be queries for one-shot data or subscriptions for events or streaming data. On the other hand, it receives, stores, and processes the returned data from network devices. Data analysis can be interactive to initiate further data queries. This component can reside in either network devices - or remote controllers. + or remote controllers. It can be centralized and distributed, and + involve one or more instances. Data Configuration and Subscription: This component deploys data queries on devices. It determines the protocol and channel for applications to acquire desired data. This component is also responsible for configuring the desired data that might not be directly available form data sources. The subscription data can be described by models, templates, or programs. Data Encoding and Export: This component determines how telemetry data are delivered to the data analysis and storage component. @@ -910,23 +955,24 @@ processing on either the fast path or the slow path in network devices. Data Object and Source: This component determines the monitoring object and original data source. The data source usually just provides raw data which needs further processing. A data source can be considered a probe. A probe can be statically installed or dynamically installed. +----------------------------------------+ - | | - | Data Query, Analysis, & Storage | - | | + +----------------------------------------+ | + | | | + | Data Query, Analysis, & Storage | | + | | + +-------+++ -----------------------------+ ||| ^^^ ||| ||| ||V ||| +--+V--------------------+++------------+ +-----V---------------------+------------+ | +---------------------+-------+----------+ | | | Data Configuration | | | | | & Subscription | Data Encoding | | | | (model, template, | & Export | | | @@ -972,21 +1018,21 @@ Complex Data: The data need to be synthesized or processed in network from raw data from one or more network devices. The data processing function can be statically or dynamically loaded into network devices. Event-triggered Data: The data are conditionally acquired based on the occurrence of some events. It can be actively pushed through subscription or passively polled through query. There are many ways to model events, including using Finite State Machine (FSM) - or Event Condition Action (ECN) [I-D.wwx-netmod-event-yang]. + or Event Condition Action (ECA) [I-D.wwx-netmod-event-yang]. Streaming Data: The data are continuously generated. It can be time series or the dump of databases. The streaming data reflect realtime network states and metrics and require large bandwidth and processing power. The streaming data are always actively pushed to the subscribers. The above data types are not mutually exclusive. Rather, they often overlap. For example, event-triggered data can be simple or complex, and streaming data can be simple, complex, or triggered by events. @@ -1048,23 +1094,23 @@ Figure 5: Existing Work Mapping I The second table is based on the telemetry modules and components. +-------------+-----------------+---------------+--------------+ | | Management | Control | Forwarding | | | Plane | Plane | Plane | +-------------+-----------------+---------------+--------------+ | data config.| gRPC, NETCONF, | NETCONF/YANG | NETCONF/YANG,| - | & subscribe | SMIv2,YANG PUSH | | YANG FSM | + | & subscribe | SMIv2,YANG PUSH | YANG PUSH | YANG PUSH | +-------------+-----------------+---------------+--------------+ - | data gen. & | DNP, | DNP, | IOAM, | + | data gen. & | DNP, | DNP, | IOAM, PSAMP | | process | YANG | YANG | PBT, IPFPM, | | | | | DNP | +-------------+-----------------+---------------+--------------+ | data | gRPC, NETCONF | BMP, NETCONF | IPFIX | | export | YANG PUSH | | | +-------------+-----------------+---------------+--------------+ Figure 6: Existing Work Mapping II 5. Evolution of Network Telemetry @@ -1072,39 +1118,40 @@ Network telemetry is a fast evolving technical area. As the network moves towards the automated operation, network telemetry undergoes several stages of evolution. Each stage is built upon the techniques enabled by previous stages. Stage 0 - Static Telemetry: The telemetry data source and type are determined at design time. The network operator can only configure how to use it with limited flexibility. Stage 1 - Dynamic Telemetry: The custom telemetry data can be - dynamically programmed or configured at runtime, allowing a - tradeoff among resource, performance, flexibility, and coverage. - DNP is an effort towards this direction. + dynamically programmed or configured at runtime without + interrupting the network operation, allowing a tradeoff among + resource, performance, flexibility, and coverage. DNP is an + effort towards this direction. Stage 2 - Interactive Telemetry: The network operator can - continuously customize the telemetry data in real time to reflect - the network operation's visibility requirements. At this stage, - some tasks can be automated, although ultimately human operators - will still need to sit in the middle to make decisions. + continuously customize and fine tune the telemetry data in real + time to reflect the network operation's visibility requirements. + Compared with Stage 1, the changes are frequent based on the real- + time feedback. At this stage, some tasks can be automated, but + human operators still need to sit in the middle to make decisions. - Stage 3 - Closed-loop Telemetry: Human operators are completely - excluded from the control loop. The intelligent network operation - engine automatically issues the telemetry data requests, analyzes - the data, and updates the network operations in closed control - loops. + Stage 3 - Closed-loop Telemetry: The telemetry is free from the + interference of human operators, except for generating the + reports. The intelligent network operation engine automatically + issues the telemetry data requests, analyzes the data, and updates + the network operations in closed control loops. The most of the existing technologies belong to stage 0 and stage 1. Individual stage 2 and stage 3 applications are also possible now. - However, the future autonomic networks may need a comprehensive operation management system which relies on stage 2 and stage 3 telemetry to cover all the network operation tasks. A well-defined network telemetry framework is the first step towards this direction. 6. Security Considerations The complexity of network telemetry raises significant security implications. For example, telemetry data can be manipulated to exhaust various network resources at each plane as well as the data @@ -1106,22 +1153,22 @@ 6. Security Considerations The complexity of network telemetry raises significant security implications. For example, telemetry data can be manipulated to exhaust various network resources at each plane as well as the data consumer; falsified or tampered data can mislead the decision making and paralyze networks; wrong configuration and programming for telemetry is equally harmful. Given that this document has proposed a framework for network - telemetry and the telemetry mechanisms discussed are distinct (in - both message frequency and traffic amount) from the conventional + telemetry and the telemetry mechanisms discussed are more extensive + (in both message frequency and traffic amount) than the conventional network OAM concepts, we must also reflect that various new security considerations may also arise. A number of techniques already exist for securing the forwarding plane, the control plane, and the management plane in a network, but it is important to consider if any new threat vectors are now being enabled via the use of network telemetry procedures and mechanisms. Security considerations for networks that use telemetry methods may include: @@ -1131,32 +1178,35 @@ telemetry capabilities; o Protocol transport used telemetry data and inherent security capabilities; o Telemetry data stores, storage encryption and methods of access; o Tracking telemetry events and any abnormalities that might identify malicious attacks using telemetry interfaces. + o Authentication and signing of telemetry data to make data more + trustworthy. + Some of the security considerations highlighted above may be minimized or negated with policy management of network telemetry. In a network telemetry deployment it would be advantageous to separate telemetry capabilities into different classes of policies, i.e., Role Based Access Control and Event-Condition-Action policies. Also, potential conflicts between network telemetry mechanisms must be detected accurately and resolved quickly to avoid unnecessary network telemetry traffic propagation escalating into an unintended or intended denial of service attack. Further study of the security issues will be required, and it is - expected that the secuirty mechanisms and protocols are devloped and + expected that the secuirty mechanisms and protocols are developed and deployed along with a network telemetry system. 7. IANA Considerations This document includes no request to IANA. 8. Contributors The other contributors of this document are listed as follows. @@ -1196,39 +1246,39 @@ [I-D.ietf-grow-bmp-adj-rib-out] Evens, T., Bayraktar, S., Lucente, P., Mi, K., and S. Zhuang, "Support for Adj-RIB-Out in BGP Monitoring Protocol (BMP)", draft-ietf-grow-bmp-adj-rib-out-07 (work in progress), August 2019. [I-D.ietf-grow-bmp-local-rib] Evens, T., Bayraktar, S., Bhardwaj, M., and P. Lucente, "Support for Local RIB in BGP Monitoring Protocol (BMP)", - draft-ietf-grow-bmp-local-rib-07 (work in progress), May - 2020. + draft-ietf-grow-bmp-local-rib-08 (work in progress), + November 2020. [I-D.ietf-ippm-ioam-data] Brockners, F., Bhandari, S., and T. Mizrahi, "Data Fields - for In-situ OAM", draft-ietf-ippm-ioam-data-10 (work in - progress), July 2020. + for In-situ OAM", draft-ietf-ippm-ioam-data-11 (work in + progress), November 2020. [I-D.ietf-netconf-distributed-notif] Zhou, T., Zheng, G., Voit, E., Graf, T., and P. Francois, "Subscription to Distributed Notifications", draft-ietf- - netconf-distributed-notif-00 (work in progress), October + netconf-distributed-notif-01 (work in progress), November 2020. [I-D.ietf-netconf-udp-notif] Zheng, G., Zhou, T., Graf, T., Francois, P., and P. Lucente, "UDP-based Transport for Configured - Subscriptions", draft-ietf-netconf-udp-notif-00 (work in - progress), October 2020. + Subscriptions", draft-ietf-netconf-udp-notif-01 (work in + progress), November 2020. [I-D.irtf-nmrg-ibn-concepts-definitions] Clemm, A., Ciavaglia, L., Granville, L., and J. Tantsura, "Intent-Based Networking - Concepts and Definitions", draft-irtf-nmrg-ibn-concepts-definitions-02 (work in progress), September 2020. [I-D.kumar-rtgwg-grpc-protocol] Kumar, A., Kolhe, J., Ghemawat, S., and L. Ryan, "gRPC Protocol", draft-kumar-rtgwg-grpc-protocol-00 (work in @@ -1240,40 +1290,39 @@ (gNMI)", draft-openconfig-rtgwg-gnmi-spec-01 (work in progress), March 2018. [I-D.pedro-nmrg-anticipated-adaptation] Martinez-Julia, P., "Exploiting External Event Detectors to Anticipate Resource Requirements for the Elastic Adaptation of SDN/NFV Systems", draft-pedro-nmrg- anticipated-adaptation-02 (work in progress), June 2018. [I-D.song-ippm-postcard-based-telemetry] - Song, H., Zhou, T., Li, Z., Shin, J., and K. Lee, - "Postcard-based On-Path Flow Data Telemetry", draft-song- - ippm-postcard-based-telemetry-07 (work in progress), April - 2020. + Song, H., Zhou, T., Li, Z., Mirsky, G., Shin, J., and K. + Lee, "Postcard-based On-Path Flow Data Telemetry using + Packet Marking", draft-song-ippm-postcard-based- + telemetry-08 (work in progress), October 2020. [I-D.song-opsawg-dnp4iq] Song, H. and J. Gong, "Requirements for Interactive Query with Dynamic Network Probes", draft-song-opsawg-dnp4iq-01 (work in progress), June 2017. [I-D.song-opsawg-ifit-framework] Song, H., Qin, F., Chen, H., Jin, J., and J. Shin, "In- situ Flow Information Telemetry", draft-song-opsawg-ifit- framework-13 (work in progress), October 2020. [I-D.wwx-netmod-event-yang] - Bierman, A., WU, Q., Bryskin, I., Birkholz, H., Liu, X., - and B. Claise, "A YANG Data model for ECA Policy - Management", draft-wwx-netmod-event-yang-09 (work in - progress), July 2020. + WU, Q., Bryskin, I., Birkholz, H., Liu, X., and B. Claise, + "A YANG Data model for ECA Policy Management", draft-wwx- + netmod-event-yang-10 (work in progress), November 2020. [RFC1157] Case, J., Fedor, M., Schoffstall, M., and J. Davin, "Simple Network Management Protocol (SNMP)", RFC 1157, DOI 10.17487/RFC1157, May 1990, . [RFC2578] McCloghrie, K., Ed., Perkins, D., Ed., and J. Schoenwaelder, Ed., "Structure of Management Information Version 2 (SMIv2)", STD 58, RFC 2578, DOI 10.17487/RFC2578, April 1999, @@ -1374,28 +1423,28 @@ In this non-normative appendix, we provide an overview of some existing techniques and standard proposals for each network telemetry module. A.1. Management Plane Telemetry A.1.1. Push Extensions for NETCONF NETCONF [RFC6241] is one popular network management protocol, which is also recommended by IETF. Although it can be used for data - collection, NETCONF is good at configurations. YANG Push + collection, NETCONF is good at configurations. YANG Push [RFC8641] + [RFC8639] extends NETCONF and enables subscriber applications to + request a continuous, customized stream of updates from a YANG + datastore. Providing such visibility into changes made upon YANG + configuration and operational objects enables new capabilities based + on the remote mirroring of configuration and operational state. - [RFC8641][RFC8639] extends NETCONF and enables subscriber - applications to request a continuous, customized stream of updates - from a YANG datastore. Providing such visibility into changes made - upon YANG configuration and operational objects enables new - capabilities based on the remote mirroring of configuration and - operational state. Moreover, distributed data collection mechanism + Moreover, distributed data collection mechanism [I-D.ietf-netconf-distributed-notif] via UDP based publication channel [I-D.ietf-netconf-udp-notif] provides enhanced efficiency for the NETCONF based telemetry. A.1.2. gRPC Network Management Interface gRPC Network Management Interface (gNMI) [I-D.openconfig-rtgwg-gnmi-spec] is a network management protocol based on the gRPC [I-D.kumar-rtgwg-grpc-protocol] RPC (Remote Procedure Call) framework. With a single gRPC service definition,