Internet DRAFT - draft-he-adaptive-collecting-problem-usecases
draft-he-adaptive-collecting-problem-usecases
Network Working Group X. He
Internet-Draft X. Mao
Intended status: Standards Track China Telecom
Expires: 6 September 2022 Q. Ma
X. Zhou
Huawei
5 March 2022
Problem Statement and Use Cases of Adaptive Traffic Data Collection
draft-he-adaptive-collecting-problem-usecases-00
Abstract
IP carrier network needs to provide real-time traffic visibility to
help network operators quickly and accurately locate network
congestion and packet loss, and make timely path adjustment for
deterministic services in order to avoid congestion. It is essential
to explore the adaptive traffic data collection mechanism so as to
capture real-time network state at minimum resource consumption.
This document summarizes the problems currently faced by network
operators when attempting to provide timely traffic data collection
to satisfy the various scenarios that require real-time network state
and traffic visibility, and aggregates the requirements for adaptive
traffic collecting mechanism from a variety of deployment scenarios.
Status of This Memo
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
1.1. Abbreviations . . . . . . . . . . . . . . . . . . . . . . 3
2. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . 4
3. Problem Statement . . . . . . . . . . . . . . . . . . . . . . 4
4. Scenarios of Adaptive Traffic data collection . . . . . . . . 6
4.1. Multi-dimensional real-time portrait of interface traffic
characteristic . . . . . . . . . . . . . . . . . . . . . 6
4.2. Microburst traffic detecting . . . . . . . . . . . . . . 6
4.3. Congestion avoidance for deterministic services . . . . . 7
4.4. On-path telemetry based on adaptive traffic sampling . . 7
5. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 8
6. Security Considerations . . . . . . . . . . . . . . . . . . . 8
7. References . . . . . . . . . . . . . . . . . . . . . . . . . 8
7.1. Normative References . . . . . . . . . . . . . . . . . . 8
7.2. Informative References . . . . . . . . . . . . . . . . . 9
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 9
1. Introduction
With the advent of cloud computing, big data and AI, as well as the
scale deployment of 5G mobile communication technology, a large
number of uRLLC services such as AR/VR, industrial Internet and
computing power network have emerged, which puts forward higher
requirements for the service quality of IP carrier network. IP
carrier network needs to provide real-time traffic visibility to help
network operators quickly and accurately locate network congestion
and packet loss, and make timely path adjustment for the services of
deterministic delay in order to avoid the congested nodes and links.
For such business scenarios, the network needs to provide traffic
sampling capability in sub seconds or even milliseconds so as to gain
real-time network state.
For decades, SNMP and MIBs have been widely deployed and the de facto
choice for many monitoring solutions, especially in collecting
interface traffic. Arguably the biggest shortcoming of SNMP for
those applications concerns the need to rely on periodic polling,
because it introduces an additional load on the network and devices,
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and it is brittle if polling cycles are missed. Therefore, SNMP has
no capability to realize real-time traffic sampling at sub seconds or
even milliseconds level. Telemetry, as a revolutionary data
acquisition technique, based on pull mechanism that is able to
deliver object changes as they happen, overcomes the limitations of
SNMP such as "slow speed, low efficiency and more demands for
processing capacity". Nevertheless, for the sake of capturing real-
time network state, persistent sampling of interface traffic at
milliseconds interval will generate a considerable amount of data
which may claim too much transport bandwidth and overload the servers
for data collection, storage, and analysis. Increasing the data
handling capacity is technically feasible but expensive, and
difficult to achieve large-scale deployment in operator's networks.
It is essential to explore the adaptive traffic data collection
mechanism so as to capture real-time network state at minimum
resource consumption.
This document summarizes the problems currently faced by network
operators when attempting to provide timely traffic data collection
to satisfy the various requirements by the aforementioned new
scenarios that require real-time network state and traffic
visibility. Also, this document aggregates the requirements for
adaptive traffic data collection mechanism from a variety of
deployment scenarios.
1.1. Abbreviations
AI: Artificial Intelligence
AR: Augmented Reality
VR: Virtual Reality
IP RAN: IP Radio Access Network
DetNet: Deterministic Networking
QoE: Quality of Experience
SLA: Service Level Agreement
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uRLLC: ultra Reliable & Low Latency Communication
NMS: Network Management System
IDC: Internet Data Center
SNMP: Simple Network Management Protocol
MIB: Management Information Base
2. Terminology
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and
"OPTIONAL" in this document are to be interpreted as described in BCP
14 [RFC2119] [RFC8174] when, and only when, they appear in all
capitals, as shown here.
The following terms are defined in this document:
adaptive traffic data collection: Allow servers automatically switch
to different telemetry sampling period to collect traffic data
according to the threshold change.
3. Problem Statement
As is well known ,IP network, based on statistical multiplexing
model, is of traffic burst characteristics. For a long time,
operators have obtained traffic visibility from the Network
Management System (NMS), and satisfied with 30~40% bandwidth
utilization. In spite of such low link usage, many complaints have
still been received about poor QoE in delivering applications with
the sensitivity of delay and packet loss. The fundamental cause is
that the observed average network traffic masks the characteristics
of traffic burst, given that SNMP is widely employed in operator's
networks to collect network traffic at 5 minutes intervals.
A large quantity of laboratory data and operational data indicate
that a microburst phenomenon occurs frequently in operator's carrier
networks, such as IP RAN, IP metropolitan network, IP backbone
network and IDC. The typical duration of such a microburst is tens
to hundreds of milliseconds, easy to cause instantaneous congestion
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of the output queue. Network congestion amplifies queuing delay and
jitter, it may even give rise to packet loss. All along, solving the
problem of network congestion is a major challenge for IP networks.
So, the microburst is not beneficial to the deterministic-delay
applications. And it is difficult to eliminate the microburst, but
must attempt to avoid it.
Although the mechanism of microburst is not very distinct, however,
it does not hinder us to detect it. Fortunately, Telemetry (e.g.,
YANG PUSH [RFC8639] [RFC8641],gNMI [gNMI]) has the capability to
collect interface traffic at a higher frequency, i.e., millisecond
interval. So, by means of telemetry technique, we can capture the
complete aspects of a microburst traffic. However, it is impractical
to gain the real-time traffic visibility at the cost of persistent
sampling at millisecond intervals. For example, in order to capture
a microburst traffic of interface, at least 10-millisecond sampling
cycle is necessary. Compared with the today's widely employed
5-minute sampling cycle based on SNMP, the required resources will
increase by 30000 times!
It is essential to investigate the adaptive traffic data collection
mechanism so as to capture real-time network state at minimum
resource consumption. That is to say, in normal non-congested
network conditions, which happen at the time of 95% above, minutes-
level sampling cycle is enough as it is. But, while detecting a
congestion state or congestion trend, sampling period must be timely
tuned to milliseconds to capture a microburst traffic of interface.
A congestion state or congestion trend of interface is manifested by
packet loss due to queue overflow, queue depth beyond the threshold
or too high link utilization, which can be defined as Event-triggered
data. Such data can be actively pushed through subscription or
passively polled through query. Although the microburst phenomenon
occurs frequently, it is transient and an on-line detection tool is
preferable to find it timely. The traditional method of using CPU on
main control board through query is processing resources consuming,
the network device must possess built-in hardware designed especially
to monitor it.
In order to reduce the excessive consumption of resources caused by
millisecond level collection of the single data, batch data such as
hundreds of sampled traffic data from an interface can be packaged as
a telemetry packet and is sent to the collector. The timestamp is
required for every sampled traffic data for the convenience of the
collector visualizing the interface traffic trend, And the collector
must realize traffic visualization in real-time manner in order that
the operators can observe it immediately.
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4. Scenarios of Adaptive Traffic data collection
This section presents several typical scenarios which require
adaptive traffic data collection to gain real-time network state and
traffic visibility at minimum resource consumption.
4.1. Multi-dimensional real-time portrait of interface traffic
characteristic
Interface traffic data collection is one of the most important
functions for NMS. Today, more and more applications are of latency-
sensitive and loss-sensitive characteristic, and the real-time
traffic visibility can help operators better understand network
performance so as to achieve SLA guarantees. On the other hand,
obtaining the holistic and genuine characteristic of interface
traffic is also a basic requirement for the statistical multiplexing
model of IP network, which is of great significance for traffic
prediction, network planning, network capacity expansion, network
optimization,etc. However, the traditional NMS based on SNMP has no
capability to depict genuine characteristic of interface traffic, and
interface traffic data collection based on telemetry techniques is
preferable.
It is essential to exploit the adaptive traffic data collection
techniques to depict multi-dimensional real-time portrait of
interface traffic characteristic at minimum resource consumption.
That is to say, in normal non-congested network conditions, which
happen at the time of 95% above, minutes-level sampling cycle is
enough as it is. But, while detecting a congestion state or
congestion trend, sampling cycle must be timely tuned to milliseconds
to capture a microburst traffic of interface. Such an adaptive
traffic data collection technique can not only reflect the coarse-
grained interface traffic characteristics, but also capture the
congestion state of interface with finer time granularity. It will
be an important tool for the DetNet to obtain real-time network
performance. Because of the lower cost, it can be deployed on large-
scale in operator's networks.
4.2. Microburst traffic detecting
Microburst traffic, as an instantaneous congestion phenomenon
occurring frequently in IP carrier network, will cause critical delay
jitter and even packet loss, which will seriously affect the QoE of
latency-sensitive and loss-sensitive applications. The ability of
detecting microburst traffic of interface will help network operators
quickly and accurately locate network congestion and packet loss, and
make timely path adjustment for deterministic-delay services in order
to avoid the congested nodes and links.
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Because the typical duration of such a microburst is generally tens
to hundreds of milliseconds, at least 10-millisecond sampling cycle
is necessary. Although the microburst phenomenon occurs frequently,
it takes very little time of 24 hours a day. It is not a good
approach to observe it through persistent millisecond sampling
period. Preferably, we can capture it as soon as a microburst occurs
to ensure important diagnose data will not be missed.Because it is
transient and an on-line detection tool is required to find it
timely. Triggered by the events such as packet loss, queue depth
beyond the threshold which is detected timely, sampling period must
be timely tuned to milliseconds to capture a microburst traffic of
interface. In a word, it is of practical significance to explore the
microburst detection technology aiming at minimizing resource
consumption.
4.3. Congestion avoidance for deterministic services
Network congestion will rapidly increase queuing delay and jitter, it
may even give rise to packet loss, which will seriously affect the
QoE of delay-sensitive and packet loss-sensitive applications. The
goal of network optimization is to reduce the occurrence of network
congestion as much as possible.
It is a complicated problem for network operators to accurately
predict the trend of network congestion and make network adjustment
in advance. The real-time traffic visibility based on the adaptive
traffic data collection techniques can accurately predict the long-
term congestion, and quickly capture the instantaneous congestion
(i.e,. microburst) of interface. By means of the real-time traffic
visibility, the automatic optimization tool (e.g., AI) can make
timely path adjustment for key traffic flows. For example, based on
the real-time traffic visibility and microburst events (e.g., packet
loss, queue depth) collected, the controller can accurately predict
the congestion trend of interface and make timely traffic redirection
to the non-congested interface for delay deterministic applications.
4.4. On-path telemetry based on adaptive traffic sampling
On-path telemetry is useful for application-aware networking
operations. For example, it is critical for the operators who offer
high-bandwidth, latency and loss-sensitive services such as video
streaming and online gaming to closely monitor the relevant flows in
real-time as the basis for any further optimizations. Applying on-
path telemetry on all packets of selected flows can still be out of
reach. A sampling rate should be set for these flows and only enable
telemetry on the sampled packets. However, a too high rate would
exhaust the network resource and even cause packet drops; an overly
low rate, on the contrary, would result in the loss of information
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and inaccuracy of measurements.
An adaptive approach can be used based on the network conditions to
dynamically adjust the sampling rate. In normal network state, a low
sampling rate is enough to reflect network performance; But, in case
of network congestion, the controller is aware of it from the real-
time traffic visibility and events data collected (e.g., packet loss,
queue depth), and timely adjust the packet sampling rate at very high
level. Even all packets of selected flows are applicable so as to
acquire real-time measurement data such as latency, jitter and packet
loss.
5. IANA Considerations
This document does not include an IANA request.
6. Security Considerations
This document provides an adaptive telemetry mechanism to minimize
the resource consumption. The increased complexity of network
telemetry may give rise to some security concerns. For example,
persistent traffic collection at very high rate (e.g., at millisecond
interval) induced by wrong configuration or spurious triggering might
exhaust resources of the forwarding plane and the control plane of
network device as well as the collector; An inappropriate threshold
setting should be avoided. The telemetry data is highly sensitive,
which exposes a lot of information about the network and its
configuration. Some of that information can make designing attacks
against the network much easier (e.g., exact details of what software
and patches have been installed), and allows an attacker to determine
whether a device may be subject to unprotected security
vulnerabilities.
On the other hand, for telemetry interfaces security considerations,
NETCONF or gNMI must provide authentication, data
integrity,confidentiality, and replay protection. And further study
of the security issues will be required.
7. References
7.1. Normative References
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119,
DOI 10.17487/RFC2119, March 1997,
<https://www.rfc-editor.org/info/rfc2119>.
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[RFC3688] Mealling, M., "The IETF XML Registry", BCP 81, RFC 3688,
DOI 10.17487/RFC3688, January 2004,
<https://www.rfc-editor.org/info/rfc3688>.
[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>.
[RFC6242] Wasserman, M., "Using the NETCONF Protocol over Secure
Shell (SSH)", RFC 6242, DOI 10.17487/RFC6242, June 2011,
<https://www.rfc-editor.org/info/rfc6242>.
[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>.
7.2. Informative References
[gNMI] "https://github.com/openconfig/gnmi".
[RFC8174] Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
May 2017, <https://www.rfc-editor.org/info/rfc8174>.
Authors' Addresses
Xiaoming He
China Telecom
Email: hexm4@chinatelecom.cn
Dongfeng Mao
China Telecom
Email: maodf@chinatelecom.cn
Qiufang Ma
Huawei
101 Software Avenue, Yuhua District
Nanjing
Jiangsu, 210012
China
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Email: maqiufang1@huawei.com
Tianran Zhou
Huawei
Email: zhoutianran@huawei.com
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