Hi,
Here is my belated AD review of draft-ietf-opsawg-ntf-07.txt.
I would like to thank you for the effort that you have put into this document,
and apologise for my long delay in reviewing it.
Broadly, I think that this is a good and useful framework, but in some of the
latter parts of the document it seems to give prominence to protocols that I
don't think have IETF consensus behind them yet (particularly DNP). I have
flagged specific comments in comments inline within the document, but I think
that the document will have been accuracy/longevity if text about the potential
technologies is mostly kept to the appendices.
There were quite a lot of cases where the text doesn't scan, or read easily,
particularly in the latter sections of this document, although I acknowledge
that none of the authors appear to be native English speakers. Ideally, these
sorts of issues would have been highlighted and addressed during WG LC.
Although the RFC editor will improve the language of the documents, making the
improvements now before IESG review will aid its passage, and hopefully result
in a better document when it is published. I have flagged and proposed
alternative text/grammar where possible. Once you have made the markups and
resolved the issues/questions that I have raised then I can run it through a
grammar checking tool (Lar's will run an equivalent tool during IESG review
anyway ...)
All of my comments are directly inline, please search for "RW" or "RW:"
OPSAWG H. Song
Internet-Draft Futurewei
Intended status: Informational F. Qin
Expires: August 23, 2021 China Mobile
P. Martinez-Julia
NICT
L. Ciavaglia
Nokia
A. Wang
China Telecom
February 19, 2021
Network Telemetry Framework
draft-ietf-opsawg-ntf-07
Abstract
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 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.
RW:
I would suggest condensing the abstract to the following and move the other
text to the introduction if it is not already covered there.
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. 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/.
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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 August 23, 2021.
Copyright Notice
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. Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . 4
3. Background . . . . . . . . . . . . . . . . . . . . . . . . . 6
3.1. Telemetry Data Coverage . . . . . . . . . . . . . . . . . 7
3.2. Use Cases . . . . . . . . . . . . . . . . . . . . . . . . 7
3.3. Challenges . . . . . . . . . . . . . . . . . . . . . . . 9
3.4. Network Telemetry . . . . . . . . . . . . . . . . . . . . 10
4. The Necessity of a Network Telemetry Framework . . . . . . . 12
5. Network Telemetry Framework . . . . . . . . . . . . . . . . . 13
5.1. Top Level Modules . . . . . . . . . . . . . . . . . . . . 14
5.1.1. Management Plane Telemetry . . . . . . . . . . . . . 17
5.1.2. Control Plane Telemetry . . . . . . . . . . . . . . . 17
5.1.3. Forwarding Plane Telemetry . . . . . . . . . . . . . 18
5.1.4. External Data Telemetry . . . . . . . . . . . . . . . 20
5.2. Second Level Function Components . . . . . . . . . . . . 21
5.3. Data Acquisition Mechanism and Type Abstraction . . . . . 22
5.4. Mapping Existing Mechanisms into the Framework . . . . . 24
6. Evolution of Network Telemetry Applications . . . . . . . . . 25
7. Security Considerations . . . . . . . . . . . . . . . . . . . 26
8. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 27
9. Contributors . . . . . . . . . . . . . . . . . . . . . . . . 27
10. Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . 28
11. Informative References . . . . . . . . . . . . . . . . . . . 28
Appendix A. A Survey on Existing Network Telemetry Techniques . 32
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A.1. Management Plane Telemetry . . . . . . . . . . . . . . . 32
A.1.1. Push Extensions for NETCONF . . . . . . . . . . . . . 32
A.1.2. gRPC Network Management Interface . . . . . . . . . . 32
A.2. Control Plane Telemetry . . . . . . . . . . . . . . . . . 33
A.2.1. BGP Monitoring Protocol . . . . . . . . . . . . . . . 33
A.3. Data Plane Telemetry . . . . . . . . . . . . . . . . . . 33
A.3.1. The Alternate Marking (AM) technology . . . . . . . . 33
A.3.2. Dynamic Network Probe . . . . . . . . . . . . . . . . 34
A.3.3. IP Flow Information Export (IPFIX) protocol . . . . . 35
A.3.4. In-Situ OAM . . . . . . . . . . . . . . . . . . . . . 35
A.3.5. Postcard Based Telemetry . . . . . . . . . . . . . . 35
A.4. External Data and Event Telemetry . . . . . . . . . . . . 35
A.4.1. Sources of External Events . . . . . . . . . . . . . 36
A.4.2. Connectors and Interfaces . . . . . . . . . . . . . . 37
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 37
1. Introduction
Network visibility is the ability of management tools to see the
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.
RW: I suggest 'historical' rather than 'conventional'
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.
RW: Rather than term of, perhaps 'the term "network telemetry" lacks an
unambiguous definition'.
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
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technologies. We then provide an architectural framework for network
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. Glossary
Before further discussion, we list some key terminology and acronyms
used in this documents. We make an intended differentiation between
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.
RW:
Nit: "this documents." -> "this document."
Nit: "as an extension" rather than "as the extension".
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].
DNP: Dynamic Network Probe, referring to programmable in-network
sensors for network monitoring and measurement.
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DPI: Deep Packet Inspection, referring to the techniques that
examines packet beyond packet L3/L4 headers.
gNMI: gRPC Network Management Interface, a network management
protocol from OpenConfig Operator Working Group, mainly
contributed by Google. See [gnmi] for details.
gRPC: gRPC Remote Procedure Call, a open source high performance RPC
framework that gNMI is based on. See [grpc] for details.
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: 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.
RW: referring to => refers to applications that allow network administrators to
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.
SMIv2 Structure of Management Information Version 2, specified in
[RFC2578].
RW:
Is SMIv2 a better reference than MIBs, that readers are more likely to be
familiar with?
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
RW:
Nit: Please drop the first sentence, and add a reference to RFC 7950.
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network management protocols such as the NETCONF and RESTCONF.
YANG is defined in [RFC6020].
YANG ECA A YANG model for Event-Condition-Action policies, defined
in [I-D.wwx-netmod-event-yang].
YANG PUSH: A method to subscribe pushed data from remote YANG
datastore on network devices. Details are specified in [RFC8641]
and [RFC8639].
RW:
Perhaps borrow from the abstract in RFC 8641.
"A mechanism that allows subscriber applications to request a
stream of updates from a YANG datastore on a network device". Details are
...
3. 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. 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.
RW:
Also need to consider privacy.
I think that we need to be careful not to imply that the intention here is to
read/snoop on the data being carried over the network rather than gather
insights into flows
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,
anomalies, and policy violations, as well as predicting future
events. In turn, the network policy updates for planning, intrusion
prevention, optimization, and self-healing may be applied.
It is conceivable that an autonomic network [RFC7575] is the logical
next step for network evolution following Software Defined Network
(SDN), aiming to reduce (or even eliminate) human labor, make more
efficient use of network resources, and provide better services more
aligned with customer requirements. Intent-based Networking (IBN)
[I-D.irtf-nmrg-ibn-concepts-definitions] requires network visibility
and telemetry data in order to ensure that the network is behaving as
intended. Although it takes time to reach the ultimate goal, the
journey has started nevertheless.
RW:
It would be helpful for the text to link autonomic networking and Intent based
networking, perhaps:
The related technique of Intent-based Networking [...] requires ...
RW:
Not sure that the last sentence of the paragraph is required.
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
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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 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 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 the umbrella term - Network Telemetry.
RW:
We should also include the possibilty of extending existing protocols, methods,
techniques.
3.1. 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 being sent to a data consumer. Such
processed data is also considered telemetry data. A classification
of telemetry data is provided in Section 5.
RW:
Specially - I would expand this. Perhaps: "In some cases, raw data is
processed before being sent .."
We should also discuss the quality of data, i.e., less, higher quality data may
be better than lots of low quality data.
3.2. Use Cases
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.
o 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 into networks.
RW:
I agree with this, but it might be good to emphasize that the goal is
to get to a place where this can be done without any, or only minimal,
human intervention.
o 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
RW:
constraint => constrain
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[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
specifying how to achieve or implement them. An intent requires a
complex translation and mapping process before being applied on
networks. While a policy or an intent is enforced, the compliance
needs to be verified and monitored continuously, relying on
visibility that is provided through network telemetry data, and
any violation needs to be reported immediately.
RW:
Does it not also rely on visibility of the network to potentially modify
the mapping to ensure that the intent remains in force?
o 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 telemetry data,
including data from network measurements.
o 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 to feed into machine learning applications.
RW:
In these sorts of scenarios, I would expect additional detailed diagnostics
information to be requested from the device to figure out the root cause. Or
specifically, I think that this would contain data that wouldn't normally be
exported via telemetry.
o 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. Long-term planning of
network capacity and topology requires analysis of real-world
network telemetry data that is obtained over long periods of time.
o Event Tracking and Prediction: The visibility into 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
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advance so proactive actions can be taken to avoid catastrophic
consequences.
3.3. 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
are not sufficient to support the above use cases for the following
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
flows to control protocol packets, from device configurations to
operations, and from physical layer to application layer.
Conventional OAM only covers a narrow range of data (e.g., SNMP
only handles data from the Management Information Base (MIB)).
Traditional network devices cannot provide all the necessary
probes. More open and programmable network devices are therefore
needed.
o Many application scenarios need to correlate network-wide data
from multiple sources (i.e., from distributed network devices,
different components of a network device, or different network
planes). A piecemeal solution is often lacking the capability to
consolidate the data from multiple sources. The composition of a
complete solution, as partly proposed by Autonomic Resource
Control Architecture(ARCA)
[I-D.pedro-nmrg-anticipated-adaptation], will be empowered and
guided by a comprehensive framework.
o Some of the conventional OAM techniques (e.g., CLI and Syslog)
lack a formal data model. The unstructured data hinder the tool
automation and application extensibility. Standardized data
models are essential to support the programmable networks.
o Although some conventional OAM techniques support data push (e.g.,
SNMP Trap [RFC2981][RFC3877], Syslog, and sFlow), the pushed data
are limited to only predefined management plane warnings (e.g.,
SNMP Trap) or sampled user packets (e.g., sFlow). Network
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operators require the data with arbitrary source, granularity, and
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.
3.4. Network Telemetry
Network telemetry has emerged as a mainstream technical term to refer
to the network data collection and consumption techniques. Several
network telemetry techniques and protocols (e.g., IPFIX [RFC7011] and
gRPC [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.
Network telemetry usually assumes machines as data consumers 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 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,
telemetry collectors subscribe to streaming data pushed from data
sources in network devices.
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o Volume and Velocity: The telemetry data is intended to be consumed
by machines rather than by human being. Therefore, the data
volume can be huge and the processing is optimized for the needs
of automation in realtime.
o Normalization and Unification: Telemetry aims to address the
overall network automation needs. Efforts are made to normalize
the data representation and unify the protocols, so to simplify
data analysis and provide integrated analysis across heterogeneous
devices and data sources across a network.
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
run continuously and adapt to the dynamic and interactive queries
from the network operation controller.
In addition, an ideal network telemetry solution may also have the
following features or properties:
o In-Network Customization: The data that is generated can be
customized in network at run-time to cater to the specific need of
applications. This needs the support of a programmable data plane
which allows probes with custom functions to be deployed at
flexible locations.
o In-Network Data Aggregation and Correlation: Network devices and
aggregation points can work out which events and what data needs
to be stored, reported, or discarded thus reducing the load on the
central collection and processing points while still ensuring that
the right information is ready to be processed in a timely way.
o In-Network Processing: Sometimes it is not necessary or feasible
to gather all information to a central point to be processed and
acted upon. It is possible for the data processing to be done in
network, allowing reactive actions to be taken locally.
o Direct Data Plane Export: The data originated from the data plane
forwarding chips can be directly exported to the data consumer for
efficiency, especially when the data bandwidth is large and the
real-time processing is required.
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o In-band Data Collection: In addition to the passive and active
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 compromised.
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.
4. The Necessity of a Network Telemetry Framework
RW: I think that the structure of the document might be better if this was a
section 3.5 of the background rather than it's own top level section?
Network data analytics and machine-learning technologies are applied
for network operation automation, relying on abundant and coherent
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 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 using an integrated, converged
mechanism and common telemetry data representations wherever
feasible. 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
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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 user experience to acquire the
comprehensive information.
o Applications require network telemetry to be elastic in order to
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 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.
5. 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. 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.
RW: Relationship of telemetry data vs get requests. I.e., isn't telemtry just
push rather than pulling data.
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5.1. 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
of the network, as shown in Figure 1. Therefore, we categorize the
network telemetry into four distinct modules with each having its own
interface to Network Operation Applications.
+------------------------------+
| |
| Network Operation |<-------+
| Applications | |
| | |
+------------------------------+ |
^ ^ ^ |
| | | |
V | V V
+-----------|---+--------------+ +-----------+
| | | | | |
| Control Pl|ane| | | External |
| Telemetry | <---> | | Data and |
| | | | | Event |
| ^ V | Management | | Telemetry |
+------|--------+ Plane | | |
| V | Telemetry | +-----------+
| Forwarding | |
| Plane <---> |
| Telemetry | |
| | |
+---------------+--------------+
Figure 1: Modules in Layer Category of NTF
RW:
In this diagram, for me at least, I think that it would more natural to have
Management Plane on the left, and Control/ Forwarding Plane on the right.
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 required data bandwidth and latency.
RW:
Data can be sent directly, or proxied via the control and management planes.
There are advantages/disadvantages to both approaches.
We summarize the major differences of the four modules in the
following table. They are compared from six angles:
o Data Object
o Data Export Location
o Data Model
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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 location where data is mostly conveniently
exported also varies. For example, forwarding plane data mainly
originates from the fast path(e.g., forwarding chips) while control
plane data mainly originates from the slow path (e.g., main control
CPU).
RW: Rather than fast/slow path, I suggest something more like:
For example, forwarding plane data mainly originates as data exported
from the forwarding ASICs, while cotnrol plane data originates from
the protocol daemons running on the control CPU(s).
For convenience and efficiency, it is preferred to export the
data from locations near the source. Because each location that can
export data has different capability, the proper data model,
encoding, and transport method cannot be kept the same.
RW: export the data => export the data off the device from
RW: capability => capabilities
RW: I don't think that it is that the data model, encoding, protocol cannot be
kept the same, but more that the difference choices are made to balance the
encoding complexity, etc.
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.
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+---------+--------------+--------------+--------------+-----------+
| 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 |
+---------+--------------+--------------+--------------+-----------+
|Export | main control | main control | fwding chip | various |
|Location | CPU, | CPU | or linecard | |
| | linecard CPU | | CPU; main | |
| | or fwding | | control CPU | |
| | chip | | unlikely | |
+---------+--------------+--------------+--------------+-----------+
|Data | YANG, | MIB, syslog, | template, | YANG |
|Model | custom | YANG, | YANG, | |
| | | custom | custom | |
+---------+--------------+--------------+--------------+-----------+
|Data | GPB, JSON, | GPB, JSON, | plain | GPB, JSON |
|Encoding | XML, plain | XML | | XML, plain|
+---------+--------------+--------------+--------------+-----------+
|Protocol | gRPC,NETCONF,| gRPC,NETCONF,| IPFIX, mirror| gRPC |
| | IPFIX,mirror | | | |
+---------+--------------+--------------+--------------+-----------+
|Transport| HTTP, TCP, | HTTP, TCP | UDP | HTTP,TCP |
| | UDP | | | UDP |
+---------+--------------+--------------+--------------+-----------+
RW:
1. Suggest removing MIB from Management Plane Object.
2. Presuming you mean ACL counters, I would put ACL under forwarding
plane rather than control plane, as in that is the source of the
data. Perhaps also add FIB to this box as well?
3. For Data Model, Management Plane, I would list YANG, MIB, syslog.
4. For Data Model, External Data, add "custom" as well as YANG?
5. gRPC, and NETFLOW and probably both worth listing as Forwarding
Plane protocols for telemetry.
Figure 2: Comparison of the Data Object Modules
Note that the interaction with the applications that consume network
telemetry data can be indirect. Some in-device data transfer is
possible. For example, in the management plane telemetry, the
management plane may need to acquire data from the data plane. Some
of the operational states can only be derived from data plane data
sources such as the interface status and statistics. For another
example, obtaining control plane telemetry data may require the
ability access the Forwarding Information Base (FIB) of the data
plane.
RW: may need to acquire => will need to acquire?
RW: For another example, => As another example,
RW: ability access => ability to access
On the other hand, an application may involve more than one plane and
interact with multiple planes simultaneously. For example, an SLA
compliance application may require both the data plane telemetry and
the control plane telemetry.
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The requirements and challenges for each module are summarized as
follows (note that the requirements may pertain across all telemetry
modules; however, we emphasize those that are most pronounced for a
particular plane).
5.1.1. 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. 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:
o Convenient Data Subscription: An application should have the
freedom to choose the data export means such as the data types and
the export frequency.
RW: What is meant by data types here?
RW: What about choosing between on-change vs periodic subscriptions?
o 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.
RW: YANG is better described as a data modelling language rather than
a schema language. I.e., "Data modeling langugages, such as YANG, can
efficiently ..."
o 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.
RW:
- Should there be any mention of exporting telemetry data directly from the
linecards out of dataplane interfaces rather than over dedicated management
ports?
- Also appllying compression to the stream of data may also be a viable
alternative to a compact encoding format.
5.1.2. Control Plane Telemetry
The control plane telemetry refers to the health condition monitoring
of different network control protocols covering Layer 2 to Layer 7.
RW: Presumably this could also cover protocols at other layers. Hence, would
it be better to say at all layers of the protocol stack?
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. Some particular challenges and issues faced by the
control plane telemetry are as follows:
RW: Suggest "operational status" rather than "running status"
RW: in fine => with fine
o One challenging problem for the control plane telemetry is how to
correlate the End-to-End (E2E) Key Performance Indicators (KPI) to
a specific layer's KPIs. For example, an IPTV user may describe
his User Experience (UE) by the video fluency and definition.
Then in case of an unusually poor UE KPI or a service
disconnection, it is non-trivial to delimit and pinpoint the issue
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in the responsible protocol layer (e.g., the Transport Layer or
the Network Layer), the responsible protocol (e.g., ISIS or BGP at
the Network Layer), and finally the responsible device(s) with
specific reasons.
o Traditional OAM-based approaches for control plane KPI measurement
include PING (L3), Tracert (L3), Y.1731 (L2), and so on. One
common issue behind these methods is that they only measure the
KPIs instead of reflecting the actual running status of these
protocols, making them less effective or efficient for control
plane troubleshooting and network optimization.
RW: Should it be "Ping" rather than PING, and Traceroute rather than Tracert?
o 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.
RW: (BMP), it is currently to => (BMP). It is used for
RW: "such as BGP" => "such as, BGP" and delete the "and so on"
RW: Rather than saying IGP monitoring is missing, isn't it that the
IGP monitoring is not as extensive as BMP?
5.1.3. Forwarding Plane Telemetry
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 originates.
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).
RW:
This is potentially a choice here. I.e., some deployments may accept a
lower peak pps performance in order to get better monitoring data.
Otherwise, the same concern could be raised regarding control plane
telemetry, where the gathering of telemetry data may affect convergence.
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).
RW: sheer data quantity should not => sheer quantity of data must not
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.
RW: Whilst this is true, I think that this equally applies to management and
control plane telemetry.
o The data should be structured and labeled, and easy for
applications to parse and consume. At the same time, the data
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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.
RW: I don't really understand the second sentence, particularly as it
relates to standards, perhaps it can be removed?
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.
RW:
The data plane programmability => Data plane programmability
Technique Taxonomy: concerning about how one instruments the
telemetry, there can be multiple possible dimensions to classify the
forwarding plane telemetry techniques.
o Active, Passive, and Hybrid: This dimension concerns about the
end-to-end measurement. 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], Alternate-Marking (AM) [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.
RW:
Suggest: network measurement results => network measurements.
o 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., AM [RFC8321]).
RW:
I suggest rewording the above paragraph to:
Telemetry data carried in user packets before being exported to a
data collector is considered in-band, e.g., in-situ OAM
[I-D.ietf-ippm-ioam-data]. Telemetry data that is directly exported
to a data collector without modifying user packets is considered
out-of-band (e.g., the postcard-based approach described in Appendix XXX).
It isalso possible to have hybrid methods, where only the telemetry
instruction or partial data is carried by user packets (e.g., AM [RFC8321]).
o E2E and In-Network: Some E2E methods start from and end at the
network end hosts (e.g., Ping). The other methods work in
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networks and are transparent to end hosts. However, if needed,
in-network methods can be easily extended into end hosts.
RW:
You have abbreviated End-to-End to E2E, but it is only used in three places,
so I would just keep it expanded in all three cases to keep the document
readble.
Also suggest:
Some E2E methods start ... => End-to-End methods start from, and end at, the
network end hosts (e.g., Ping)
The other methods => In-Network methods
Perhaps: in-network => In-Network
o Data Subject: 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.
5.1.4. External Data Telemetry
Events that occur outside the boundaries of the network system are
another important source of network telemetry. Correlating both
internal telemetry data and external events with the requirements of
network systems, as presented in
[I-D.pedro-nmrg-anticipated-adaptation], provides a strategic and
functional advantage to management operations.
As with other sources of telemetry information, the data and events
must meet strict requirements, especially in terms of timeliness,
which is essential to properly incorporate external event information
to management cycles. The specific challenges are described as
follows:
RW:
I'm not sure what is meant by management cycles. Hence, I suggest something
like: to management cycles => into network management applications
o The role of external event detector can be played by multiple
elements, including hardware (e.g. physical sensors, such as
seismometers) and software (e.g. Big Data sources that analyze
streams of information, such as Twitter messages). Thus, the
transmitted data must support different shapes but, at the same
time, follow a common but extensible schema.
RW:
of external => of the external
o Since the main function of the external event detectors is to
perform the notifications, their timeliness is assumed. However,
once messages have been dispatched, they must be quickly collected
and inserted into the control plane with variable priority, which
will be high for important sources and/or important events and low
for secondary ones.
RW:
It is unclear to me what is being acted on here. Is this telemetry information
flowing into a controller or somewhere else?
o The schema used by external detectors must be easily adopted by
current and future devices and applications. Therefore, it must
be easily mapped to current information models, such as in terms
of YANG.
RW:
Do you mean information model here, or data model (given that YANG is a data
modelling language)?
Organizing together both internal and external telemetry information
will be key for the general exploitation of the management
possibilities of current and future network systems, as reflected in
the incorporation of cognitive capabilities to new hardware and
software (virtual) elements.
RW:
I would suggest rephasing this to:
Organizing both internal and external telemetry information together will be
key ...
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5.2. Second Level Function Components
Reflecting the best current practice, the telemetry module at each
plane is further partitioned into five distinct components:
RW:
I would suggest rephasing this to:
The telemetry module as each plane can be further partitioned into five
distinct conceptual components:
o Data Query, Analysis, and Storage: This component works at the
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. It can be centralized and distributed, and
involve one or more instances.
RW:
Given that you say that this can be on the device, I suggest: It is a part ...
=> It is normally a part ...
o 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.
RW:
Rather than "deploys" would it better to say that it manages data queries on
devices.
o Data Encoding and Export: This component determines how telemetry
data is delivered to the data analysis and storage component. The
data encoding and the transport protocol may vary due to the data
exporting location.
RW:
data exporting => data export location.
o Data Generation and Processing: The requested data needs to be
captured, processed, and formatted in network devices from raw
data sources. This may involve in-network computing and
processing on either the fast path or the slow path in network
devices.
RW:
Is this the component that would be responsible filtering the data (if
required)?
o 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. Some data sources can be dynamically
installed, while others will be more static.
RW:
Does this architecture envisage one data source or multiple data
sources? I would assume multiple data source components. Perhaps the
description could be clarified to make this more clear?
RW:
- Which of these components are responsible for handling access control to the
data?
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+----------------------------------------+
+----------------------------------------+ |
| | |
| Data Query, Analysis, & Storage | |
| | +
+-------+++ -----------------------------+
||| ^^^
||| |||
||V |||
+--+V--------------------+++------------+
+-----V---------------------+------------+ |
+---------------------+-------+----------+ | |
| Data Configuration | | | |
| & Subscription | Data Encoding | | |
| (model, template, | & Export | | |
| & program) | | | |
+---------------------+------------------| | |
| | | |
| Data Generation | | |
| & Processing | | |
| | | |
+----------------------------------------| | |
| | | |
| Data Object and Source | |-+
| |-+
+----------------------------------------+
Figure 3: Components in the Network Telemetry Framework
5.3. Data Acquisition Mechanism and Type Abstraction
Broadly speaking, network data can be acquired through subscription
(push) and query (poll). A subscription is a contract between
publisher and subscriber. After initial setup, the subscribed data
is automatically delivered to registered subscribers until the
subscription expires. Subscription can be partitioned into two sub
modes: the Publish-Subscription (Pub-Sub) mode and the Subscription-
Publish (Sub-Pub) mode. In the Pub-Sub mode, a publisher publishes
pre-defined data and any qualified subscribers can subscribe the data
as-is. In the Sub-Pub mode, a subscriber initiates a data request
and sends it to a publisher; the publisher will deliver the requested
data when available. While for both modes, the subscribed data is
pushed to the subscriber, the Sub-Pub mode allows subscribers to
customize their subscriptions.
RW:
subscribe the data => subscribe to the data
I don't think that it good to try and distinguish between pub/sub and sub/pub
and make them two distinct things.
I think that it would be better to phrase this just in terms of pub/sub with
variation on whether the subscriptions are pre-defined, or the subscriber can
configure and tailor the published data to their specific needs.
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In contrast, query is used when a querier expects immediate and one-
off feedback from network devices. The queried data may be directly
extracted from some specific data source, or synthesized and
processed from raw data. Query suits for interactive network
telemetry applications.
RW:
query is used => queries are used
Suggest: querier => client.
Query suits for => Queries work well for
There are four types of data from network devices that a telemetry
data consumer can subscribe or query:
o Simple Data: The data that are steadily available from some data
store or static probes in network devices. such data can be
specified by YANG model.
RW:
data store => datastore
such data => Such data
But I'm not sure that YANG should be mentioned here at all, given that the
other types of data mentioned here may also be modelled in YANG.
o 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.
RW:
Rather than calling it Complex data, Derived data might be a better term.
I.e., to indicate that the data is being derived from other sources of simple
or derived data.
o 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 (ECA) [I-D.wwx-netmod-event-yang].
RW:
Would it be helpful to give an example of event-triggered data? E.g., a network
interface changing operational state for up to down.
o 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.
RW:
Calling this "Sampled Data" might be better than "Streaming Data". I regard
streaming as being about how the data is being returned, not want the source of
the data is. Again, giving an example might be helpful here, e.g., an
interface packet counter, which is read every 10 seconds.
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.
The relationships of these data types are illustrated in Figure 4.
RW:
I would think that every source of data is either simple or derived (complex)
and it is either event driven, or sampled (streamed).
I.e., I don't really understand what the diagram below is trying to convey. At
least for me, it would probably be more intuitive if the diagram was the other
way up. I.e., have simple data at the bottom of the diagram.
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+--------------+
+------>| Simple Data |<------+
| +------------- + |
| ^ |
| | |
| +------+-------+ |
| +-->| Complex Data |<--+ |
| | +--------------+ | |
| | | |
| | | |
+-------+---+----------+ +-----+---+-------+
| Event-triggered Data |<----+ Streaming Data |
+----------------------+ +-----------------+
Figure 4: Data Type Relationship
Subscription usually deals with event-triggered data and streaming
data, and query usually deals with simple data and complex data. But
the other ways are also possible. The conventional OAM techniques
are mostly about querying simple data. While these techniques are
still useful, more advanced network telemetry techniques are designed
mainly for event-triggered or streaming data subscription, and
complex data query.
RW:
I think that this mixes sources of data with how they are accessed. E.g., for
many years operators have polled interface counters or a regular cadence which
effectively generates a stream of values.
I think that the key point is that the data can be pulled, but in many cases,
pushing the data is more efficient, and can reduce the latency of a client
detecting a change in the operational state.
5.4. Mapping Existing Mechanisms into the Framework
The following two tables show how the existing mechanisms (mainly
published in IETF and with the emphasis on the latest new
technologies) are positioned in the framework. Given the vast body
of existing work, we cannot provide an exhaustive list, so the
mechanisms in the tables should be considered as just examples.
Also, some comprehensive protocols and techniques may cover multiple
aspects or modules of the framework, so a name in a block only
emphasizes one particular characteristic of it. More details about
some listed mechanisms can be found in Appendix A.
The first table is based on the data acquisition mechanisms and data
types.
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+----------------------+-----------+--------------+
| | Query | Subscription |
+----------------------+-----------+--------------+
| Simple Data | SNMP | YANG |
+----------------------+-----------+--------------+
| Complex Data | DNP | YANG PUSH |
+----------------------+-----------+--------------+
| Event-triggered Data | DNP | YANG PUSH |
+----------------------+-----------+--------------+
| Streaming Data | N/A | gRPC |
+----------------------+-----------+--------------+
RW:
I'm not sure convinced that DNP should be in this table at all (I'm not
familiar with it).
For subscriptions do you mean gRPC or gNMI, or perhaps both?
I would regard SNMP, RESTCONF, NETCONF as all being viable ways of querying
data, but it is less effective for event-triggered data. I would regard YANG
Push and gNMI to both be equivalent technologies for subscriptions to any of
the data sources, regardless of whether it is simple or derived, and
event-driven or sampled.
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 PUSH | YANG PUSH |
+-------------+-----------------+---------------+--------------+
| data gen. & | DNP, | DNP, | IOAM, PSAMP |
| process | YANG | YANG | PBT, AM, |
| | | | DNP |
+-------------+-----------------+---------------+--------------+
| data | gRPC, NETCONF | BMP, NETCONF | IPFIX |
| export | YANG PUSH | | |
+-------------+-----------------+---------------+--------------+
RW:
I suggest YANG PUSH => YANG-Push throughout this document (for consistency with
RFC 8641).
Regarding this diagram, I'm not entirely sure how helpful it is - I find it
slightly hard to understand exactly what it is trying to convey. But some
potential suggestions:
Should SMIv2 be replaced with SNMP?
Should SMIv2 (or MIBs) be added to data gen. & process?
What's the differentiation between NETCONF and NETCONF/YANG? Do you just mean
NETCONF in those places?
Should it be gNMI rather than gRPC in the top row, and should it be present in
the top row for Control Plane?
For Data Export, should that be things like: gRPC, HTTP, TCP, UDP + XML, JSON,
CBOR, Protobufs? Perhaps the box label should be Data export and encoding?
Again, I'm not sure whether DNP should be a part of this diagram. Has that
work been adopted by any WG yet? Or whether DNP would be best left in the
appendix and then added to this diagram in a future version of this document if
it gets standardized.
Figure 6: Existing Work Mapping II
6. Evolution of Network Telemetry Applications
Network telemetry is a fast evolving technical area. As the network
moves towards the automated operation, network telemetry applications
undergo several stages of evolution which add new layer of
requirements to the underlying network telemetry techniques. Each
stage is built upon the techniques adopted by the previous stages
plus some new requirements.
RW:
Nit, "fast evolving" -> "evolving".
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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 without
interrupting the network operation, allowing a tradeoff among
resource, performance, flexibility, and coverage. DNP is an
effort towards this direction.
RW:
- I think that it would be better to leave out DNP here, it seems out of
keeping with the rest of the text. Perhaps in the appendix where DNP is
described it could reference back to this text?
Stage 2 - Interactive Telemetry: The network operator can
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: 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.
Existing technologies are ready for 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 works at stage 2 and stage 3 to cover all the
network operation tasks. A well-defined network telemetry framework
is the first step towards this direction.
7. 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.
RW:
I would say that the telemetry data is highly sensitive.
Telemetry exposes a lot of information about the network and its
configuration, and some of that information could make designing
attacks against the network much easier (e.g., exact details of what
software has been installed (including patches), could allow an attacker
to determine whether a device may be subject to unprotected security
vulnerability)
Given that this document has proposed a framework for network
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.
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Security considerations for networks that use telemetry methods may
include:
o Telemetry framework trust and policy model;
o Role management and access control for enabling and disabling
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.
RW:
Also, separating the management & telemetry traffic from the data traffic
carried over the network. E.g., historically management access and
management data may be carried via an independent management network.
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 developed and
deployed along with a network telemetry system.
RW:
I think that this document may benefit from a short section on privacy,
i.e., pointing out the balancing required between managing and maintaining the
network vs the privacy of users of that network.
8. IANA Considerations
This document includes no request to IANA.
9. Contributors
The other contributors of this document are listed as follows.
o Tianran Zhou
o Zhenbin Li
o Zhenqiang Li
o Daniel King
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o Adrian Farrel
o Alexander Clemm
10. Acknowledgments
We would like to thank Greg Mirsky, Randy Presuhn, Joe Clarke, Victor
Liu, James Guichard, Uri Blumenthal, Giuseppe Fioccola, Yunan Gu,
Parviz Yegani, Young Lee, Qin Wu, and many others who have provided
helpful comments and suggestions to improve this document.
11. Informative References
[gnmi] "gNMI - gRPC Network Management Interface",
<https://github.com/openconfig/reference/tree/master/rpc/
gnmi>.
[grpc] "gPPC, A high performance, open-source universal RPC
framework", <https://grpc.io>.
[I-D.fioccola-ippm-multipoint-alt-mark]
Fioccola, G., Cociglio, M., Sapio, A., and R. Sisto,
"Multipoint Alternate Marking method for passive and
hybrid performance monitoring", draft-fioccola-ippm-
multipoint-alt-mark-04 (work in progress), June 2018.
[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-09 (work in progress),
January 2021.
[I-D.ietf-ippm-ioam-data]
Brockners, F., Bhandari, S., and T. Mizrahi, "Data Fields
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-01 (work in progress), November
2020.
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[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-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
progress), July 2016.
[I-D.openconfig-rtgwg-gnmi-spec]
Shakir, R., Shaikh, A., Borman, P., Hines, M., Lebsack,
C., and C. Morrow, "gRPC Network Management Interface
(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., 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.
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[I-D.wwx-netmod-event-yang]
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,
<https://www.rfc-editor.org/info/rfc1157>.
[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,
<https://www.rfc-editor.org/info/rfc2578>.
[RFC2981] Kavasseri, R., Ed., "Event MIB", RFC 2981,
DOI 10.17487/RFC2981, October 2000,
<https://www.rfc-editor.org/info/rfc2981>.
[RFC3416] Presuhn, R., Ed., "Version 2 of the Protocol Operations
for the Simple Network Management Protocol (SNMP)",
STD 62, RFC 3416, DOI 10.17487/RFC3416, December 2002,
<https://www.rfc-editor.org/info/rfc3416>.
[RFC3594] Duffy, P., "PacketCable Security Ticket Control Sub-Option
for the DHCP CableLabs Client Configuration (CCC) Option",
RFC 3594, DOI 10.17487/RFC3594, September 2003,
<https://www.rfc-editor.org/info/rfc3594>.
[RFC3877] Chisholm, S. and D. Romascanu, "Alarm Management
Information Base (MIB)", RFC 3877, DOI 10.17487/RFC3877,
September 2004, <https://www.rfc-editor.org/info/rfc3877>.
[RFC4656] Shalunov, S., Teitelbaum, B., Karp, A., Boote, J., and M.
Zekauskas, "A One-way Active Measurement Protocol
(OWAMP)", RFC 4656, DOI 10.17487/RFC4656, September 2006,
<https://www.rfc-editor.org/info/rfc4656>.
[RFC5357] Hedayat, K., Krzanowski, R., Morton, A., Yum, K., and J.
Babiarz, "A Two-Way Active Measurement Protocol (TWAMP)",
RFC 5357, DOI 10.17487/RFC5357, October 2008,
<https://www.rfc-editor.org/info/rfc5357>.
[RFC6020] Bjorklund, M., Ed., "YANG - A Data Modeling Language for
the Network Configuration Protocol (NETCONF)", RFC 6020,
DOI 10.17487/RFC6020, October 2010,
<https://www.rfc-editor.org/info/rfc6020>.
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[RFC6241] Enns, R., Ed., Bjorklund, M., Ed., Schoenwaelder, J., Ed.,
and A. Bierman, Ed., "Network Configuration Protocol
(NETCONF)", RFC 6241, DOI 10.17487/RFC6241, June 2011,
<https://www.rfc-editor.org/info/rfc6241>.
[RFC6812] Chiba, M., Clemm, A., Medley, S., Salowey, J., Thombare,
S., and E. Yedavalli, "Cisco Service-Level Assurance
Protocol", RFC 6812, DOI 10.17487/RFC6812, January 2013,
<https://www.rfc-editor.org/info/rfc6812>.
[RFC7011] Claise, B., Ed., Trammell, B., Ed., and P. Aitken,
"Specification of the IP Flow Information Export (IPFIX)
Protocol for the Exchange of Flow Information", STD 77,
RFC 7011, DOI 10.17487/RFC7011, September 2013,
<https://www.rfc-editor.org/info/rfc7011>.
[RFC7276] Mizrahi, T., Sprecher, N., Bellagamba, E., and Y.
Weingarten, "An Overview of Operations, Administration,
and Maintenance (OAM) Tools", RFC 7276,
DOI 10.17487/RFC7276, June 2014,
<https://www.rfc-editor.org/info/rfc7276>.
[RFC7540] Belshe, M., Peon, R., and M. Thomson, Ed., "Hypertext
Transfer Protocol Version 2 (HTTP/2)", RFC 7540,
DOI 10.17487/RFC7540, May 2015,
<https://www.rfc-editor.org/info/rfc7540>.
[RFC7575] Behringer, M., Pritikin, M., Bjarnason, S., Clemm, A.,
Carpenter, B., Jiang, S., and L. Ciavaglia, "Autonomic
Networking: Definitions and Design Goals", RFC 7575,
DOI 10.17487/RFC7575, June 2015,
<https://www.rfc-editor.org/info/rfc7575>.
[RFC7799] Morton, A., "Active and Passive Metrics and Methods (with
Hybrid Types In-Between)", RFC 7799, DOI 10.17487/RFC7799,
May 2016, <https://www.rfc-editor.org/info/rfc7799>.
[RFC7854] Scudder, J., Ed., Fernando, R., and S. Stuart, "BGP
Monitoring Protocol (BMP)", RFC 7854,
DOI 10.17487/RFC7854, June 2016,
<https://www.rfc-editor.org/info/rfc7854>.
[RFC8321] Fioccola, G., Ed., Capello, A., Cociglio, M., Castaldelli,
L., Chen, M., Zheng, L., Mirsky, G., and T. Mizrahi,
"Alternate-Marking Method for Passive and Hybrid
Performance Monitoring", RFC 8321, DOI 10.17487/RFC8321,
January 2018, <https://www.rfc-editor.org/info/rfc8321>.
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[RFC8639] Voit, E., Clemm, A., Gonzalez Prieto, A., Nilsen-Nygaard,
E., and A. Tripathy, "Subscription to YANG Notifications",
RFC 8639, DOI 10.17487/RFC8639, September 2019,
<https://www.rfc-editor.org/info/rfc8639>.
[RFC8641] Clemm, A. and E. Voit, "Subscription to YANG Notifications
for Datastore Updates", RFC 8641, DOI 10.17487/RFC8641,
September 2019, <https://www.rfc-editor.org/info/rfc8641>.
Appendix A. A Survey on Existing Network Telemetry Techniques
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 [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
[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.
RW:
I suggest rewording the first two sentences to something like:
NETCONF [RFC6241] is a popular network management protocol,
recommended by IETF. Its core strength is for managing configuration,
but can also be used for data collection.
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,
both configuration and telemetry can be covered. gRPC is an HTTP/2
[RFC7540] based open source micro service communication framework.
It provides a number of capabilities which are well-suited for
network telemetry, including:
o Full-duplex streaming transport model combined with a binary
encoding mechanism provided further improved telemetry efficiency.
RW:
provided further improved => provides good telemetry
o gRPC provides higher-level features consistency across platforms
that common HTTP/2 libraries typically do not. This
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characteristic is especially valuable for the fact that telemetry
data collectors normally reside on a large variety of platforms.
o The built-in load-balancing and failover mechanism.
A.2. Control Plane Telemetry
A.2.1. BGP Monitoring Protocol
BGP Monitoring Protocol (BMP) [RFC7854] is used to monitor BGP
sessions and intended to provide a convenient interface for obtaining
route views.
RW:
and intended => and is intended
The BGP routing information is collected from the monitored device(s)
to the BMP monitoring station by setting up the BMP TCP session. The
BGP peers are monitored by the BMP Peer Up and Peer Down
Notifications. The BGP routes (including Adjacency_RIB_In [RFC7854],
Adjacency_RIB_out [I-D.ietf-grow-bmp-adj-rib-out], and Local_Rib
[I-D.ietf-grow-bmp-local-rib] are encapsulated in the BMP Route
Monitoring Message and the BMP Route Mirroring Message, in the form
of both initial table dump and real-time route update. In addition,
BGP statistics are reported through the BMP Stats Report Message,
which could be either timer triggered or event-driven. More BMP
extensions can be explored to enrich the applications of BGP
monitoring.
RW:
, in the form ... .=>
, providing both an initial table dump and real-time route updates.
RW:
I suggest:
More BMP extensions can be explored ... =>
Future BMP extension could further enrich BGP monitoring applications.
A.3. Data Plane Telemetry
A.3.1. The Alternate Marking (AM) technology
The Alternate Marking method is efficient to perform packet loss,
delay, and jitter measurements both in an IP and Overlay Networks, as
presented in [RFC8321] and [I-D.fioccola-ippm-multipoint-alt-mark].
RW:
I suggest:
The Alternate Marking method enables efficient measurements of
packet loss, delay, and jitter both in IP and Overlay Networks, as
presented in [RFC8321] and [I-D.fioccola-ippm-multipoint-alt-mark].
RW:
It looks like this is now draft-ietf-ippm-multipoint-alt-mark.
This technique can be applied to point-to-point and multipoint-to-
multipoint flows. Alternate Marking creates batches of packets by
alternating the value of 1 bit (or a label) of the packet header.
These batches of packets are unambiguously recognized over the
network and the comparison of packet counters for each batch allows
the packet loss calculation. The same idea can be applied to delay
measurement by selecting ad hoc packets with a marking bit dedicated
for delay measurements.
Alternate Marking method needs two counters each marking period for
each flow under monitor. For instance, by considering n measurement
points and m monitored flows, the order of magnitude of the packet
counters for each time interval is n*m*2 (1 per color).
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Since networks offer rich sets of network performance measurement
data (e.g packet counters), traditional approaches run into
limitations. One reason is the fact that the bottleneck is the
generation and export of the data and the amount of data that can be
reasonably collected from the network. In addition, management tasks
related to determining and configuring which data to generate lead to
significant deployment challenges.
RW:
One reason is the fact that the bottleneck => One bottleneck
Multipoint Alternate Marking approach, described in
[I-D.fioccola-ippm-multipoint-alt-mark], aims to resolve this issue
and makes the performance monitoring more flexible in case a detailed
analysis is not needed.
RW:
Multipoint => The Multipoint
and makes => and make
An application orchestrates network performance measurements tasks
across the network to allow an optimized monitoring and it can
calibrate how deep can be obtained monitoring data from the network
by configuring measurement points roughly or meticulously.
RW:
Suggest rewording to something like:
An application orchestrates network performance measurements tasks
across the network to allow for optimized monitoring. The application
can choose how roughly or precisely to configure measurement points
depending on the application's requirements.
Using Alternate Marking, it is possible to monitor a Multipoint
Network without examining in depth by using the Network Clustering
(subnetworks that are portions of the entire network that preserve
the same property of the entire network, called clusters). So in
case there is packet loss or the delay is too high the filtering
criteria could be specified more in order to perform a detailed
analysis by using a different combination of clusters up to a per-
flow measurement as described in Alternate-Marking (AM) [RFC8321].
RW:
Suggest tweaking the text to something like:
Using Alternate Marking, it is possible to monitor a Multipoint
Network without in depth examination by using the Network Clustering
(subnetworks that are portions of the entire network that preserve
the same property of the entire network, called clusters). So in
the case that there is packet loss or the delay is too high then
the specific filtering criteria could be applied to gather a more
detailed analysis by using a different combination of clusters up to
a per-flow measurement as described in Alternate-Marking (AM) [RFC8321].
In summary, an application can configure end-to-end network
monitoring. If the network does not experiment issues, this
approximate monitoring is good enough and is very cheap in terms of
network resources. However, in case of problems, the application
becomes aware of the issues from this approximate monitoring and, in
order to localize the portion of the network that has issues,
configures the measurement points more exhaustively. So a new
detailed monitoring is performed. After the detection and resolution
of the problem the initial approximate monitoring can be used again.
RW:
experiment => experience
exhaustively. So a new detailed monitoring is performed. => extensively,
allowing more detailed monitoring to be performed.
problem the => problem, the
A.3.2. Dynamic Network Probe
Hardware-based Dynamic Network Probe (DNP) [I-D.song-opsawg-dnp4iq]
provides a programmable means to customize the data that an
application collects from the data plane. A direct benefit of DNP is
the reduction of the exported data. A full DNP solution covers
several components including data source, data subscription, and data
generation. The data subscription needs to define the complex data
which can be composed and derived from the raw data sources. The
data generation takes advantage of the moderate in-network computing
to produce the desired data.
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While DNP can introduce unforeseeable flexibility to the data plane
telemetry, it also faces some challenges. It requires a flexible
data plane that can be dynamically reprogrammed at run-time. The
programming API is yet to be defined.
RW:
provides => proposes
A.3.3. IP Flow Information Export (IPFIX) protocol
Traffic on a network can be seen as a set of flows passing through
network elements. IP Flow Information Export (IPFIX) [RFC7011]
provides a means of transmitting traffic flow information for
administrative or other purposes. A typical IPFIX enabled system
includes a pool of Metering Processes collects data packets at one or
more Observation Points, optionally filters them and aggregates
information about these packets. An Exporter then gathers each of
the Observation Points together into an Observation Domain and sends
this information via the IPFIX protocol to a Collector.
RW:
Metering Processes collects => Metering Processes that collects
A.3.4. In-Situ OAM
Traditional passive and active monitoring and measurement techniques
are either inaccurate or resource-consuming. It is preferable to
directly acquire data associated with a flow's packets when the
packets pass through a network. In-situ OAM (iOAM)
[I-D.ietf-ippm-ioam-data], a data generation technique, embeds a new
instruction header to user packets and the instruction directs the
network nodes to add the requested data to the packets. Thus, at the
path end, the packet's experience gained on the entire forwarding
path can be collected. Such firsthand data is invaluable to many
network OAM applications.
However, iOAM also faces some challenges. The issues on performance
impact, security, scalability and overhead limits, encapsulation
difficulties in some protocols, and cross-domain deployment need to
be addressed.
A.3.5. Postcard Based Telemetry
PBT [I-D.song-ippm-postcard-based-telemetry] is an alternative to
IOAM. PBT directly exports data at each node through an independent
packet. PBT solves several issues of IOAM. It can also help to
identify packet drop location in case a packet is dropped on its
forwarding path.
RW:
is an alternative => is a proposed alternative.
Allow PBT presumably solves some issues with IOAM, I assume that there are
other compromises being made here, i.e., a potentially large increase in
telemetry packets in the network?
A.4. External Data and Event Telemetry
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A.4.1. Sources of External Events
To ensure that the information provided by external event detectors
and used by the network management solutions is meaningful for the
management purposes, the network telemetry framework must ensure that
such detectors (sources) are easily connected to the management
solutions (sinks). This requires the specification of a simple
taxonomy of detectors and match it to the connectors and/or
interfaces required to connect them.
RW:
for the management => for management
I'm not really convinced that a taxonomy of detectors is really required.
Rather, I think that you a providing a list of potential external data
sources that could be of interest in network management.
Once detectors are classified in such taxonomy, their definitions are
enlarged with the qualities and other aspects used to handle them and
represented in the ontology and information model (e.g. YANG).
Therefore, differentiating several types of detectors as potential
sources of external events is essential for the integrity of the
management framework. We thus differentiate the following source
types of external events:
RW:
As above, I'm not sure that the above paragraph really adds anything, and
perhaps could be replaced with something like:
Categories of external event sources that may be of interest to network
management include:
o Smart objects and sensors. With the consolidation of the Internet
of Things~(IoT) any network system will have many smart objects
attached to its physical surroundings and logical operation
environments. Most of these objects will be essentially based on
sensors of many kinds (e.g. temperature, humidity, presence) and
the information they provide can be very useful for the management
of the network, even when they are not specifically deployed for
such purpose. Elements of this source type will usually provide a
specific protocol for interaction, especially one of those
protocols related to IoT, such as the Constrained Application
Protocol (CoAP). It will be used by the telemetry framework to
interact with the relevant objects.
RW:
I would remove the last sentence, given it is possible that the IOT sensor
data may be collected and republished in an easier to consume form, rather
than the network telemetry framework necessarily directly interacting with
the sensors.
o Online news reporters. Several online news services have the
ability to provide enormous quantity of information about
different events occurring in the world. Some of those events can
impact on the network system managed by a specific framework and,
therefore, it will be interested on getting such information. For
instance, diverse security reports, such as the Common
Vulnerabilities and Exposures (CVE), can be issued by the
corresponding authority and used by the management solution to
update the managed system if needed. Instead of a specific
protocol and data format, the sources of this kind of information
usually follow a relaxed but structured format. This format will
be part of both the ontology and information model of the
telemetry framework.
RW:
it will be interested on getting such information. =>
such information may be of interest to the management solution.
o Global event analyzers. The advance of Big Data analyzers
provides a huge amount of information and, more interestingly, the
identification of events detected by analyzing many data streams
Song, et al. Expires August 23, 2021 [Page 36]
Internet-Draft Network Telemetry Framework February 2021
from different origins. In contrast with the other types of
sources, which are focused in specific events, the detectors of
this source type will detect very generic events. For example, a
sports event takes place and some unexpected movement makes it
highly interesting and many people connects to sites that are
covering such event. The systems supporting the services that
cover the event can be affected by such situation so their
management solutions should be aware of it. In contrast with the
other source types, a new information model, format, and reporting
protocol is required to integrate the detectors of this type with
the management solution.
RW:
focused in specific events => focused on specific events
very generic events => generic events
covering such event => reporting on the event.
The systems supporting => The underlying networks supporting
Additional types of detector types can be added to the system but
they will be generally the result of composing the properties offered
by these main classes. In any case, future revisions of the network
telemetry framework will include the required types that cover new
circumstances and that cannot be obtained by composition.
RW:
I would delete the last sentence, I don't think that it is useful.
A.4.2. Connectors and Interfaces
For allowing external event detectors to be properly integrated with
other management solutions, both elements must expose interfaces and
protocols that are subject to their particular objective. Since
external event detectors will be focused on providing their
information to their main consumers, which generally will not be
limited to the network management solutions, the framework must
include the definition of the required connectors for ensuring the
interconnection between detectors (sources) and their consumers
within the management systems (sinks) are effective.
In some situations, the interconnection between the external event
detectors and the management system is via the management plane. For
those situations there will be a special connector that provides the
typical interfaces found in most other elements connected to the
management plane. For instance, the interfaces will accomplish with
a specific information model (YANG) and specific telemetry protocol,
such as NETCONF, SNMP, or gRPC.
RW:
will accomplish with => could accomplish this with
information model (YANG) => data model (YANG)
I'm not sure that I would describe SNMP as a telemetry protocol, hence
I would suggest listing YANG Push and gRPC as example telemetry protocols.
Authors' Addresses
Haoyu Song
Futurewei
2330 Central Expressway
Santa Clara
USA
Email: [email protected]
Song, et al. Expires August 23, 2021 [Page 37]
Internet-Draft Network Telemetry Framework February 2021
Fengwei Qin
China Mobile
No. 32 Xuanwumenxi Ave., Xicheng District
Beijing, 100032
P.R. China
Email: [email protected]
Pedro Martinez-Julia
NICT
4-2-1, Nukui-Kitamachi
Koganei, Tokyo 184-8795
Japan
Email: [email protected]
Laurent Ciavaglia
Nokia
Villarceaux 91460
France
Email: [email protected]
Aijun Wang
China Telecom
Beiqijia Town, Changping District
Beijing, 102209
P.R. China
Email: [email protected]
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