OPSAWG H. Song
Internet-Draft Futurewei
Intended status: Informational F. Qin
Expires: October 15, 2020 China Mobile
P. Martinez-Julia
NICT
L. Ciavaglia
Nokia
A. Wang
China Telecom
April 13, 2020
Network Telemetry Framework
draft-ietf-opsawg-ntf-03
Abstract
Network telemetry is the technology for gaining network insight and
facilitating efficient and automated network management. It engages
various techniques for remote data collection, correlation, and
consumption. This document provides an architectural framework for
network telemetry, motivated by the network operation challenges and
requirements. As evidenced by some key characteristics and industry
practices, network telemetry covers technologies and protocols beyond
the conventional network Operations, Administration, and Management
(OAM). It promises better flexibility, scalability, accuracy,
coverage, and performance and allows automated control loops to suit
both today's and tomorrow's network operation. This document
clarifies the terminologies and classifies the modules and components
of a network telemetry system from several different perspectives.
The framework and taxonomy help to set a common ground for the
collection of related work and provide guidance for related technique
and standard developments.
Status of This Memo
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and may be updated, replaced, or obsoleted by other documents at any
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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 October 15, 2020.
Copyright Notice
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Motivation . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.1. Use Cases . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2. Challenges . . . . . . . . . . . . . . . . . . . . . . . 6
2.3. Glossary . . . . . . . . . . . . . . . . . . . . . . . . 7
2.4. Network Telemetry . . . . . . . . . . . . . . . . . . . . 8
3. The Necessity of a Network Telemetry Framework . . . . . . . 10
4. Network Telemetry Framework . . . . . . . . . . . . . . . . . 11
4.1. Data Acquiring Mechanisms and Data Types . . . . . . . . 12
4.2. Data Object Modules . . . . . . . . . . . . . . . . . . . 13
4.2.1. Requirements and Challenges for each Module . . . . . 16
4.3. Function Components . . . . . . . . . . . . . . . . . . . 19
4.4. Existing Works Mapped in the Framework . . . . . . . . . 21
5. Evolution of Network Telemetry . . . . . . . . . . . . . . . 22
6. Security Considerations . . . . . . . . . . . . . . . . . . . 23
7. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 24
8. Contributors . . . . . . . . . . . . . . . . . . . . . . . . 24
9. Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . 24
10. Informative References . . . . . . . . . . . . . . . . . . . 25
Appendix A. A Survey on Existing Network Telemetry Techniques . 28
A.1. Management Plane Telemetry . . . . . . . . . . . . . . . 28
A.1.1. Push Extensions for NETCONF . . . . . . . . . . . . . 28
A.1.2. gRPC Network Management Interface . . . . . . . . . . 28
A.2. Control Plane Telemetry . . . . . . . . . . . . . . . . . 29
A.2.1. BGP Monitoring Protocol . . . . . . . . . . . . . . . 29
A.3. Data Plane Telemetry . . . . . . . . . . . . . . . . . . 29
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A.3.1. The IPFPM technology . . . . . . . . . . . . . . . . 29
A.3.2. Dynamic Network Probe . . . . . . . . . . . . . . . . 30
A.3.3. IP Flow Information Export (IPFIX) protocol . . . . . 31
A.3.4. In-Situ OAM . . . . . . . . . . . . . . . . . . . . . 31
A.3.5. Postcard Based Telemetry . . . . . . . . . . . . . . 31
A.4. External Data and Event Telemetry . . . . . . . . . . . . 32
A.4.1. Sources of External Events . . . . . . . . . . . . . 32
A.4.2. Connectors and Interfaces . . . . . . . . . . . . . . 33
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 33
1. Introduction
Network visibility is the ability of management tools to see the
state and behavior of a network. It is essential for successful
network operation. Network telemetry is the process of measuring,
correlating, recording, and distributing information about the
behavior of a network. Network telemetry has been considered as an
ideal means to gain sufficient network visibility with better
flexibility, scalability, accuracy, coverage, and performance than
some conventional network Operations, Administration, and Management
(OAM) techniques.
However, the term of network telemetry lacks a solid and unambiguous
definition. The scope and coverage of it cause confusion and
misunderstandings. It is beneficial to clarify the concept and
provide a clear architectural framework for network telemetry, so we
can articulate the technical field, and better align the related
techniques and standard works.
To fulfill such an undertaking, we first discuss some key
characteristics of network telemetry which set a clear distinction
from the conventional network OAM and show that some conventional OAM
technologies can be considered a subset of the network telemetry
technologies. We then provide an architectural framework for network
telemetry from three different perspectives. We show how network
telemetry can meet the current and future network operation
requirements, and the challenges each telemetry module is facing.
Based on the distinction of modules and function components, we can
map the existing and emerging techniques and protocols into the
framework. At last, we outline a road-map for the evolution of the
network telemetry system and discuss the potential security concerns
for network telemetry.
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.
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2. Motivation
The term "big data" is used to describe the extremely large volume of
data sets that can be analyzed computationally to reveal patterns,
trends, and associations. Network is undoubtedly a source of big
data because of its scale and all the traffic goes through it. It is
easy to see that network OAM can benefit from network big data.
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 intent-driven 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. Although it
takes time to reach the ultimate goal, the journey has started
nevertheless.
However, while the data processing capability is improved and
applications are hungry for more data, the networks lag behind in
extracting and translating network data into useful and actionable
information in efficient ways. The system bottleneck is shifting
from data consumption to data supply. Both the number of network
nodes and the traffic bandwidth keep increasing at a fast pace. The
network configuration and policy change at smaller time slots than
before. More subtle events and fine-grained data through all network
planes need to be captured and exported in real time. In a nutshell,
it is a challenge to get enough high-quality data out of network
efficiently, timely, and flexibly. Therefore, we need to examine the
existing network technologies and protocols, and identify any
potential technique and standard gaps based on the real network and
device architectures.
In the remaining of this section, first 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,
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techniques, and protocols which we assign under an umbrella term -
network telemetry.
2.1. Use Cases
These use cases are essential for network operations. While the list
is by no means exhaustive, it is enough to highlight the requirements
for data velocity, variety, volume, and veracity in networks.
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. An intent is a high-level abstract policy which
requires a complex translation and mapping process before being
applied on networks. While a policy is enforced, the compliance
needs to be verified and monitored continuously, and any violation
needs to be reported immediately.
SLA Compliance: A Service-Level Agreement (SLA) defines the level of
service a user expects from a network operator, which include the
metrics for the service measurement and remedy/penalty procedures
when the service level misses the agreement. Users need to check
if they get the service as promised and network operators need to
evaluate how they can deliver the services that can meet the SLA
based on realtime network measurement.
Root Cause Analysis: Any network failure can be the cause or effect
of a sequence of chained events. Troubleshooting and recovery
require quick identification of the root cause of any observable
issues. However, the root cause is not always straightforward to
identify, especially when the failure is sporadic and the related
and unrelated events are overwhelming and interleaved. While
machine learning technologies can be used for root cause analysis,
it up to the network to sense and provide the relevant data.
Network Optimization: This covers all short-term and long-term
network optimization techniques, including load balancing, Traffic
Engineering (TE), and network planning. Network operators are
motivated to optimize their network utilization and differentiate
services for better Return On Investment (ROI) or lower Capital
Expenditures (CAPEX). The first step is to know the real-time
network conditions before applying policies for traffic
manipulation. In some cases, micro-bursts need to be detected in
a very short time-frame so that fine-grained traffic control can
be applied to avoid network congestion. The long-term network
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capacity planning and topology augmentation rely on the
accumulated data of network operations.
Event Tracking and Prediction: The visibility of traffic path and
performance is critical for services and applications that rely on
healthy network operation. Numerous related network events are of
interest to network operators. For example, Network operators
want to learn where and why packets are dropped for an application
flow. They also want to be warned of issues in advance so
proactive actions can be taken to avoid catastrophic consequences.
2.2. 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.
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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
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.
2.3. Glossary
Before further discussion, we list some key terminology and acronyms
used in this documents. We make an intended distinction between
network telemetry and network OAM.
AI: Artificial Intelligence. In network domain, AI refers to the
machine-learning based technologies for automated network
operation and other tasks.
BMP: BGP Monitoring Protocol, specified in [RFC7854].
DNP: Dynamic Network Probe, referring to programmable in-network
sensors for network monitoring and measurement.
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].
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IPFPM: IP Flow Performance Measurement method, specified in
[RFC8321].
IOAM: In-situ OAM, a dataplane on-path telemetry technique.
NETCONF: Network Configuration Protocol, specified in [RFC6241].
Network Telemetry: Acquiring and processing network data remotely
for network monitoring and operation. A general term for a large
set of network visibility techniques and protocols, with the
characteristics defined in this document. Network telemetry
addresses the current network operation issues and enables smooth
evolution toward future intent-driven autonomous networks.
NMS: Network Management System, referring to applications that allow
network administrators manage a network's software and hardware
components. It usually records data from a network's remote
points to carry out central reporting to a system administrator.
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.
SNMP: Simple Network Management Protocol. Version 1 and 2 are
specified in [RFC1157] and [RFC3416], respectively.
YANG: The abbreviation of "Yet Another Next Generation". YANG is a
data modeling language for the definition of data sent over
network management protocols such as the NETCONF and RESTCONF.
YANG is defined in [RFC6020].
YANG FSM: A YANG model that describes events, operations, and finite
state machine of YANG-defined network elements.
YANG PUSH: A method to subscribe pushed data from remote YANG
datastore on network devices.
2.4. Network Telemetry
Network telemetry has emerged as a mainstream technical term to refer
to the newer data collection and consumption techniques,
distinguishing itself from the convention techniques for network OAM.
The representative techniques and protocols include IPFIX [RFC7011]
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and gPRC [grpc]. Network telemetry allows separate entities to
acquire data from network devices so that data can be visualized and
analyzed to support network monitoring and operation. Network
telemetry overlaps with the conventional network OAM and has a wider
scope than it. It is expected that network telemetry can provide the
necessary network insight for autonomous networks and address the
shortcomings of conventional OAM techniques.
One difference between the network telemetry and the network OAM is
that in general the network telemetry assumes machines as data
consumer rather than human operators. Hence, the network telemetry
can directly trigger the automated network operation, while the
conventional OAM tools usually help human operators to monitor and
diagnose the networks and guide manual network operations. The
difference leads to very different techniques.
Although the network telemetry techniques are just emerging and
subject to continuous evolution, several characteristics of network
telemetry have been well accepted. Note that network telemetry is
intended to be an umbrella term covering a wide spectrum of
techniques, so the following characteristics are not expected to be
held by every specific technique.
o Push and Streaming: Instead of polling data from network devices,
the telemetry collector subscribes to the streaming data pushed
from data sources in network devices.
o Volume and Velocity: The telemetry data is intended to be consumed
by machines rather than by human being. Therefore, the data
volume is huge and the processing is often in realtime.
o Normalization and Unification: Telemetry aims to address the
overall network automation needs. The piecemeal solutions offered
by the conventional OAM approach are no longer suitable. Efforts
need to be made to normalize the data representation and unify the
protocols.
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.
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In addition, an ideal network telemetry solution may also have the
following features or properties:
o In-Network Customization: The data 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 and Action: 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, and 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.
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 defied.
Although in many cases a network telemetry system is akin to the SDN
architecture, it is important to understand that network telemetry
does not infer the need of any centralized data processing and
analytics engine. Telemetry data producers and consumers can
perfectly work in distributed or peer-to-peer fashions instead.
3. The Necessity of a Network Telemetry Framework
Big data analytics and machine-learning based AI technologies are
applied for network operation automation, relying on abundant data
from networks. The single-sourced and static data acquisition cannot
meet the data requirements. It is desirable to have a framework that
integrates multiple telemetry approaches from different layers. This
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allows flexible combinations for different applications. The
framework would benefit application development for the following
reasons:
o The future autonomous networks will require a holistic view on
network visibility. All the use cases and applications need to be
supported uniformly and coherently under a single intelligent
agent. Therefore, the protocols and mechanisms should be
consolidated into a minimum yet comprehensive set. A telemetry
framework can help to normalize the technique developments.
o Network visibility presents multiple viewpoints. For example, the
device viewpoint takes the network infrastructure as the
monitoring object from which the network topology and device
status can be acquired; the traffic viewpoint takes the flows or
packets as the monitoring object from which the traffic quality
and path can be acquired. An application may need to switch its
viewpoint during operation. It may also need to correlate a
service and its impact on network experience to acquire the
comprehensive information.
o Applications require network telemetry to be elastic in order to
efficiently use the network resource and reduce the performance
impact. Routine network monitoring covers the entire network with
low data sampling rate. When issues arise or trends emerge, the
telemetry data source can be modified and the data rate can be
boosted.
o Efficient data fusion is critical for applications to reduce the
overall quantity of data and improve the accuracy of analysis.
A telemetry framework collects together all of the telemetry-related
works from different sources and working groups within IETF. This
makes it possible to assemble a comprehensive network telemetry
system and to avoid repetitious or redundant work. The framework
should cover the concepts and components from the standardization
perspective. This document clarifies the layered modules on which
the telemetry is exerted and decomposes the telemetry system into a
set of distinct components that the existing and future work can
easily map to.
4. Network Telemetry Framework
Network telemetry techniques can be classified from multiple
dimensions. In this document, we provide three unique perspectives:
data acquiring mechanisms, data objects, and function components.
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4.1. Data Acquiring Mechanisms and Data Types
Broadly speaking, network data can be acquired through subscription
(push) and query (poll). A subscriber may request data when it is
ready. It follows a Publish-Subscription (Pub-Sub) mode or a
Subscription-Publish (Sub-Pub) mode. In the Pub-Sub mode, pre-
defined data are published and multiple qualified subscribers can
subscribe the data. In the Sub-Pub mode, a subscriber designates
what data are of interest and demands the network devices to deliver
the data when available.
In contrast, query is used when a querier expects immediate 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.
There are four types of data from network devices:
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.
Complex Data: The data need to be synthesized or processed in
network from raw data from one or more network devices. The data
processing function can be statically or dynamically loaded into
network devices.
Event-triggered Data: The data are conditionally acquired based on
the occurrence of some events. An event can be modeled as a
Finite State Machine (FSM).
Streaming Data: The data are continuously or periodically 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 above data types are not mutually exclusive. For example, event-
triggered data can be simple or complex, and streaming data can be
event triggered. The relationships of these data types are
illustrated in Figure 1.
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+--------------------------+
| +----------------------+ |
| | +-----------------+ | |
| | | +-------------+ | | |
| | | | Simple Data | | | |
| | | +-------------+ | | |
| | | Complex Data | | |
| | +-----------------+ | |
| | Event-triggered Data | |
| +----------------------+ |
| Streaming Data |
+--------------------------+
Figure 1: Data Type Relationship
Subscription usually deals with event-triggered data and streaming
data, and query usually deals with simple data and complex data. 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.
4.2. Data Object 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 2. Therefore, we categorize the
network telemetry into four distinct modules with each having its own
interface to Network Operation Applications.
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+------------------------------+
| |
| Network Operation |<-------+
| Applications | |
| | |
+------------------------------+ |
^ ^ ^ |
| | | |
V | V V
+-----------|---+--------------+ +-----------+
| | | | | |
| Control Pl|ane| | | External |
| Telemetry | <---> | | Data and |
| | | | | Event |
| ^ V | Management | | Telemetry |
+------|--------+ Plane | | |
| V | Telemetry | +-----------+
| Forwarding | |
| Plane <---> |
| Telemetry | |
| | |
+---------------+--------------+
Figure 2: Modules in Layer Category of NTF
The rationale of this partition lies in the different telemetry data
objects which result in different data source and export locations.
Such differences have profound implications on in-network data
programming and processing capability, data encoding and transport
protocol, and data bandwidth and latency.
We summarize the major differences of the four modules in the
following table. They are compared from six aspects: data object,
data export location, data model, data encoding, telemetry protocol,
and transport method. Data object is the target and source of each
module. Because the data source varies, the data export location
varies. Because each data export location has different capability,
the proper data model, encoding, and transport method cannot be kept
the same. As a result, the suitable telemetry protocol for each
module can be different. Some representative techniques are shown in
the corresponding table blocks to highlight the technical diversity
of these modules. The key point is that one cannot expect to use a
universal protocol to cover all the network telemetry requirements.
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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,| gPRC,NETCONF,| IPFIX, mirror| gRPC |
| | IPFIX,mirror | | | |
+---------+--------------+--------------+--------------+-----------+
|Transport| HTTP, TCP, | HTTP, TCP | UDP | HTTP,TCP |
| | UDP | | | UDP |
+---------+--------------+--------------+--------------+-----------+
Figure 3: Comparison of the Data Object Modules
Note that the interaction with the network operation applications 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 the data plane such as the interface status and
statistics. For another example, the control plane telemetry may
need to access the Forwarding Information Base (FIB) in data plane.
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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4.2.1. Requirements and Challenges for each Module
4.2.1.1. Management Plane Telemetry
The management plane of network elements interacts with the Network
Management System (NMS), and provides information such as performance
data, network logging data, network warning and defects data, and
network statistics and state data. Some legacy protocols, such as
SNMP and Syslog, are widely used for the management plane. However,
these protocols are insufficient to meet the requirements of the
future automated network operation applications.
New management plane telemetry protocols should consider the
following requirements:
Convenient Data Subscription: An application should have the freedom
to choose the data export means such as the data types and the
export frequency.
Structured Data: For automatic network operation, machines will
replace human for network data comprehension. The schema
languages such as YANG can efficiently describe structured data
and normalize data encoding and transformation.
High Speed Data Transport: In order to retain the information, a
server needs to send a large amount of data at high frequency.
Compact encoding formats are needed to compress the data and
improve the data transport efficiency. The subscription mode, by
replacing the query mode, reduces the interactions between clients
and servers and helps to improve the server's efficiency.
4.2.1.2. Control Plane Telemetry
The control plane telemetry refers to the health condition monitoring
of different network control protocols covering Layer 2 to Layer 7.
Keeping track of the running status of these protocols is beneficial
for detecting, localizing, and even predicting various network
issues, as well as network optimization, in real-time and in fine
granularity.
One of the most challenging problems 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 in
the responsible protocol layer (e.g., the Transport Layer or the
Network Layer), the responsible protocol (e.g., ISIS or BGP at the
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Network Layer), and finally the responsible device(s) with specific
reasons.
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.
An example of the control plane telemetry is the BGP monitoring
protocol (BMP), it is currently used to monitoring the BGP routes and
enables rich applications, such as BGP peer analysis, AS analysis,
prefix analysis, security analysis, and so on. However, the
monitoring of other layers, protocols and the cross-layer, cross-
protocol KPI correlations are still in their infancy (e.g., the IGP
monitoring is missing), which require further research.
4.2.1.3. Data Plane Telemetry
An effective data plane telemetry system relies on the data that the
network device can expose. The data's quality, quantity, and
timeliness must meet some stringent requirements. This raises some
challenges to the network data plane devices where the first hand
data originate.
o A data plane device's main function is user traffic processing and
forwarding. While supporting network visibility is important, the
telemetry is just an auxiliary function, and it should not impede
normal traffic processing and forwarding (i.e., the performance is
not lowered and the behavior is not altered due to the telemetry
functions).
o The network operation applications requires end-to-end visibility
from various sources, which results in a huge volume of data.
However, the sheer data quantity should not stress the network
bandwidth, regardless of the data delivery approach (i.e., through
in-band or out-of-band channels).
o The data plane devices must provide timely data with the minimum
possible delay. Long processing, transport, storage, and analysis
delay can impact the effectiveness of the control loop and even
render the data useless.
o The data should be structured and labeled, and easy for
applications to parse and consume. At the same time, the data
types needed by applications can vary significantly. The data
plane devices need to provide enough flexibility and
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programmability to support the precise data provision for
applications.
o The data plane telemetry should support incremental deployment and
work even though some devices are unaware of the system. This
challenge is highly relevant to the standards and legacy networks.
The data plane programmability is essential to support network
telemetry. Newer data plane forwarding chips are equipped with
advanced telemetry features and provide flexibility to support
customized telemetry functions.
4.2.1.3.1. Technique Taxonomy
There can be multiple possible dimensions to classify the data plane
telemetry techniques.
Active, Passive, and Hybrid: The active and passive methods (as well
as the hybrid types) are well documented in [RFC7799]. The
passive methods include TCPDUMP, IPFIX [RFC7011], sflow, and
traffic mirror. These methods usually have low data coverage.
The bandwidth cost is very high in order to improve the data
coverage. On the other hand, the active methods include Ping,
Traceroute, OWAMP [RFC4656], and TWAMP [RFC5357]. These methods
are intrusive and only provide indirect network measurement
results. The hybrid methods, including in-situ OAM
[I-D.ietf-ippm-ioam-data], IPFPM [RFC8321], and Multipoint
Alternate Marking [I-D.fioccola-ippm-multipoint-alt-mark], provide
a well-balanced and more flexible approach. However, these
methods are also more complex to implement.
In-Band and Out-of-Band: The telemetry data, before being exported
to some collector, can be carried in user packets. Such methods
are considered in-band (e.g., in-situ OAM
[I-D.ietf-ippm-ioam-data]). If the telemetry data is directly
exported to some collector without modifying the user packets,
such methods are considered out-of-band (e.g., postcard-based
INT). It is possible to have hybrid methods. For example, only
the telemetry instruction or partial data is carried by user
packets (e.g., IPFPM [RFC8321]).
E2E and In-Network: Some E2E methods start from and end at the
network end hosts (e.g., Ping). The other methods work in
networks and are transparent to end hosts. However, if needed,
the in-network methods can be easily extended into end hosts.
Flow, Path, and Node: Depending on the telemetry objective, the
methods can be flow-based (e.g., in-situ OAM
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[I-D.ietf-ippm-ioam-data]), path-based (e.g., Traceroute), and
node-based (e.g., IPFIX [RFC7011]).
4.2.1.4. 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:
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.
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.
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.
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.
4.3. Function Components
The telemetry module at each plane can be further partitioned into
five distinct components:
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Data Query, Analysis, and Storage: This component works at the
application layer. On the one hand, it is responsible for issuing
data requirements. The data of interest can be modeled data
through configuration or custom data through programming. The
data requirements can be queries for one-shot data or
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.
Data Configuration and Subscription: This component deploys data
queries on devices. It determines the protocol and channel for
applications to acquire desired data. This component is also
responsible for configuring the desired data that might not be
directly available form data sources. The subscription data can
be described by models, templates, or programs.
Data Encoding and Export: This component determines how telemetry
data are delivered to the data analysis and storage component.
The data encoding and the transport protocol may vary due to the
data exporting location.
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.
Data Object and Source: This component determines the monitoring
object and original data source. The data source usually just
provides raw data which needs further processing. A data source
can be considered a probe. A probe can be statically installed or
dynamically installed.
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+----------------------------------------+
| |
| Data Query, Analysis, & Storage |
| |
+----------------------------------------+
| ^
| |
V |
+---------------------+------------------+
| Data Configuration | |
| & Subscription | Data Encoding |
| (model, template, | & Export |
| & program) | |
+---------------------+------------------|
| |
| Data Generation |
| & Processing |
| |
+----------------------------------------|
| |
| Data Object and Source |
| |
+----------------------------------------+
Figure 4: Components in the Network Telemetry Framework
4.4. Existing Works Mapped in the Framework
The following two tables provide a non-exhaustive list of existing
works (mainly published in IETF and with the emphasis on the latest
new technologies) and shows their positions in the framework. More
details can be found in Appendix A.
The first table is based on the data acquiring mechanisms and data
types.
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+-----------------+---------------+----------------+
| | Query | Subscription |
| | | |
+-----------------+---------------+----------------+
| Simple Data | SNMP, NETCONF,| SNMP, NETCONF |
| | YANG, BMP, | YANG, gRPC |
| | gRPC | |
+-----------------+---------------+----------------+
| Complex Data | DNP, YANG FSM | DNP, YANG PUSH |
| | gRPC, NETCONF | gPRC, NETCONF |
+-----------------+---------------+----------------+
| Event-triggered | | gRPC, NETCONF, |
| Data | N/A | YANG PUSH, DNP |
| | | YANG FSM |
+-----------------+---------------+----------------+
| Streaming Data | | gRPC, NETCONF, |
| | N/A | IOAM, PBT, DNP |
| | | IPFIX, IPFPM |
+-----------------+---------------+----------------+
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, |
| & subscrib. | YANG PUSH | | YANG FSM |
+--------------+---------------+----------------+---------------+
| data gen. & | DNP, | DNP, | IOAM, |
| processing | YANG | YANG | PBT, IPFPM, |
| | | | DNP |
+--------------+---------------+----------------+---------------+
| data | gRPC, NETCONF | BMP, NETCONF | IPFIX |
| export | YANG PUSH | | |
+--------------+---------------+----------------+---------------+
Figure 6: Existing Work Mapping II
5. Evolution of Network Telemetry
Network telemetry is a fast evolving technical area. As the network
moves towards the automated operation, network telemetry undergoes
several levels of evolution.
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Level 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.
Level 1 - Dynamic Telemetry: The telemetry data can be dynamically
programmed or configured at runtime, allowing a tradeoff among
resource, performance, flexibility, and coverage. DNP is an
effort towards this direction.
Level 2 - Interactive Telemetry: The network operator can
continuously customize the telemetry data in real time to reflect
the network operation's visibility requirements. At this level,
some tasks can be automated, although ultimately human operators
will still need to sit in the middle to make decisions.
Level 3 - Closed-loop Telemetry: Human operators are completely
excluded from the control loop. The intelligent network operation
engine automatically issues the telemetry data requests, analyzes
the data, and updates the network operations in closed control
loops.
While most of the existing technologies belong to level 0 and level
1, with the help of a clearly defined network telemetry framework, we
are now possible to assemble the technologies to support level 2 and
make solid steps towards level 3.
6. Security Considerations
Given that this document has proposed a framework for network
telemetry and the telemetry mechanisms discussed are distinct (in
both message frequency and traffic amount) from the conventional
network OAM concepts, we must also reflect that various new security
considerations may also arise. A number of techniques already exist
for securing the forwarding plane, the control plane, and the
management plane in a network, but it is important to consider if any
new threat vectors are now being enabled via the use of network
telemetry procedures and mechanisms.
Security considerations for networks that use telemetry methods may
include:
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;
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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.
Some of the security considerations highlighted above may be
minimized or negated with policy management of network telemetry. In
a network telemetry deployment it would be advantageous to separate
telemetry capabilities into different classes of policies, i.e., Role
Based Access Control and Event-Condition-Action policies. Also,
potential conflicts between network telemetry mechanisms must be
detected accurately and resolved quickly to avoid unnecessary network
telemetry traffic propagation escalating into an unintended or
intended denial of service attack.
Further study of the security issues will be required, and it is
expected that the secuirty mechanisms and protocols are devloped and
deployed along with a network telemetry system.
7. IANA Considerations
This document includes no request to IANA.
8. Contributors
The other contributors of this document are listed as follows.
o Tianran Zhou
o Zhenbin Li
o Zhenqiang Li
o Daniel King
o Adrian Farrel
9. Acknowledgments
We would like to thank Randy Presuhn, Joe Clarke, Victor Liu, James
Guichard, Uri Blumenthal, Giuseppe Fioccola, Yunan Gu, Parviz Yegani,
Young Lee, Alexander Clemm, Qin Wu, and many others who have provided
helpful comments and suggestions to improve this document.
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10. 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-06 (work in progress),
November 2019.
[I-D.ietf-ippm-ioam-data]
Brockners, F., Bhandari, S., Pignataro, C., Gredler, H.,
Leddy, J., Youell, S., Mizrahi, T., Mozes, D., Lapukhov,
P., remy@barefootnetworks.com, r., daniel.bernier@bell.ca,
d., and J. Lemon, "Data Fields for In-situ OAM", draft-
ietf-ippm-ioam-data-09 (work in progress), March 2020.
[I-D.ietf-netconf-udp-pub-channel]
Zheng, G., Zhou, T., and A. Clemm, "UDP based Publication
Channel for Streaming Telemetry", draft-ietf-netconf-udp-
pub-channel-05 (work in progress), March 2019.
[I-D.ietf-netconf-yang-push]
Clemm, A. and E. Voit, "Subscription to YANG Datastores",
draft-ietf-netconf-yang-push-25 (work in progress), May
2019.
[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.
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[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., Shin, J., and K. Lee,
"Postcard-based On-Path Flow Data Telemetry", draft-song-
ippm-postcard-based-telemetry-06 (work in progress),
October 2019.
[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-11 (work in progress), March 2020.
[I-D.zhou-netconf-multi-stream-originators]
Zhou, T., Zheng, G., Voit, E., and A. Clemm, "Subscription
to Multiple Stream Originators", draft-zhou-netconf-multi-
stream-originators-10 (work in progress), November 2019.
[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>.
[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>.
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[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>.
[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>.
[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>.
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[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>.
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
[I-D.ietf-netconf-yang-push] 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.zhou-netconf-multi-stream-originators] via UDP based publication
channel [I-D.ietf-netconf-udp-pub-channel] provides enhanced
efficiency for the NETCONF based telemetry.
A.1.2. gRPC Network Management Interface
gRPC Network Management Interface (gNMI)
[I-D.openconfig-rtgwg-gnmi-spec] is a network management protocol
based on the gRPC [I-D.kumar-rtgwg-grpc-protocol] RPC (Remote
Procedure Call) framework. With a single gRPC service definition,
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:
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o Full-duplex streaming transport model combined with a binary
encoding mechanism provided further improved telemetry efficiency.
o gRPC provides higher-level features consistency across platforms
that common HTTP/2 libraries typically do not. This
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.
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.
A.3. Data Plane Telemetry
A.3.1. The IPFPM 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 IPFPM [RFC8321] and
[I-D.fioccola-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.
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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).
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.
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.
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.
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 IPFPM [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.
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
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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.
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.
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.
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.
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A.4. External Data and Event Telemetry
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.
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:
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.
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.
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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
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.
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.
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.
Authors' Addresses
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Haoyu Song
Futurewei
2330 Central Expressway
Santa Clara
USA
Email: hsong@futurewei.com
Fengwei Qin
China Mobile
No. 32 Xuanwumenxi Ave., Xicheng District
Beijing, 100032
P.R. China
Email: qinfengwei@chinamobile.com
Pedro Martinez-Julia
NICT
4-2-1, Nukui-Kitamachi
Koganei, Tokyo 184-8795
Japan
Email: pedro@nict.go.jp
Laurent Ciavaglia
Nokia
Villarceaux 91460
France
Email: laurent.ciavaglia@nokia.com
Aijun Wang
China Telecom
Beiqijia Town, Changping District
Beijing, 102209
P.R. China
Email: wangaj.bri@chinatelecom.cn
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