Internet DRAFT - draft-ding-opsawg-wavelength-use-case
draft-ding-opsawg-wavelength-use-case
Opsawg Working Group X. Ding
Internet-Draft W. Liu
Intended status: Informational Huawei
Expires: May 3, 2018 C. Li
China Telecom
October 30, 2017
Network Data Use Case for Wavelength Division Service
draft-ding-opsawg-wavelength-use-case-00
Abstract
This document describes use cases that demonstrate the applicability
of network data to evaluate the performance of wavelength division
service. The objective of this draft is not to cover the wavelength
division service in detail. Rather, the intention is to illustrate
the requirements of network data used to evaluate the performance of
wavelength division service.
General characteristics of network data and two typical use cases are
presented in this document to demonstrate the different application
scenarios of network data in wavelength division service.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Conventions used in this document . . . . . . . . . . . . . . 3
3. Characteristics of network data . . . . . . . . . . . . . . . 3
4. Use cases . . . . . . . . . . . . . . . . . . . . . . . . . . 4
4.1. Anomaly detection . . . . . . . . . . . . . . . . . . . . 4
4.2. Risk assessment . . . . . . . . . . . . . . . . . . . . . 5
5. Data Issues . . . . . . . . . . . . . . . . . . . . . . . . . 6
5.1. Merge data from different time periods . . . . . . . . . 6
6. Security Considerations . . . . . . . . . . . . . . . . . . . 6
7. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . 7
8. Normative References . . . . . . . . . . . . . . . . . . . . 7
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 7
1. Introduction
Wavelength-division multiplexing (WDM) is a method of combining
multiple signals on laser beams at various infrared (IR) wavelengths
for transmission along fiber optic media. A WDM system uses a
multiplexer at the transmitter to join the several signals together,
and a demultiplexer at the receiver to split them apart. During the
wavelength division service running, network data is consistently
generated from wavelength division devices and it can reflect the
process of service running.
In the case of wavelength division service, customer is accustomed to
handle the network failure after the service interruption. Such
passive strategy is inefficient, and easily leads to long service
interruption. Network data collected from device is real and
reliable, and can help customer to predict the trend of wavelength
division optical performance. Statistical characteristics of network
data can help operator to judge the time point at which the service
is abnormal or normal, or the service is risky or healthy .
This document attempts to describe the detailed use cases that lead
to the requirements to support wavelength division performance
evaluation. The objective of this draft is not to cover the
wavelength division service in detail. Rather, the intention is to
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illustrate the requirements of network data used to evaluate the
performance of wavelength division service.
General characteristics of network data and two typical use cases are
presented in this document to demonstrate the different application
scenarios of network data in wavelength division service. Moreover,
the question of how to integrate network data collected from
different time periods is raised.
2. Conventions used in this document
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
document are to be interpreted as described in RFC 2119 [RFC2119].
KPI: Key Performance Indicator. Network KPI represents the
operational state of a network device, link or network protocol in
the network. KPI data is usually represented to users as a set of
time series
(e.g., KPI = x_i, i=1..t),
each time series is corresponding to one network KPI indicator value
at different time point during specific time period.
3. Characteristics of network data
Network data describes the process that information collected from
various data sources and transmitted to one or more receiving
equipment for analysis tasks [I-D.ietf-wu-t2trg-network-telemetry].
Analysis tasks may include event correlation, anomaly detection, risk
detection, performance monitoring, trend analysis, and other related
processes.
Network data is a series of data points indexed in time order. It
taken over time may have an internal structure (such as, trend,
seasonal variation, or outliers). Trend means that, on average, the
measurements tend to increase (or decrease) over time. Seasonality
means that, there is a regularly repeating pattern of highs and lows
related to calendar time such as seasons, quarters, months, days of
the week, and so on. In regression, outliers are far away from the
line. With time series data, outliers are far away from the other
data.
Network time series data analysis comprises methods for analyzing
time series data in order to extract meaningful statistics and other
characteristics of the data.
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Network data mainly consists several major characteristics:
o Subject: The subject is the object to be measured, and it has
multiple properties from different dimensions. An example of a
wavelength division service performance monitoring scenario is
that the subject of the measurement is the ' optical module '
whose attributes may include board name, device name, and so on.
o Measured values: A subject may have one or more measured values,
and each measurement corresponds to a specific indicator. Take
the server status monitoring scenario example, the measured
indicators may have FEC_bef (Forward Error Correction coding
before error correction), FEC_aft (Forward Error Correction coding
after error correction), input optical power, output optical
power, etc.
o Timestamp: Each report of the measured value will have a timestamp
attribute to indicate its time.
4. Use cases
The following sections highlight some of the most common wavelength
division use case scenarios and are in no way exhaustive.
4.1. Anomaly detection
In Data Analytics Engine, anomaly detection is the identification of
items, events or observations which do not conform to an expected
pattern or other items in data. Typically the anomalous items will
translate to some kind of problem, such as optical layer problem.
For network equipment performance anomalies, multiple features are
usually extracted from KPI data, such as time, value, frequency,
etc., and used as the key factors for anomaly analysis.
Take wavelength division service as an example, collection
information such as FEC_bef, input optical power, laser bias current
and other key factors can be selected to keep track of wavelength
division service over time and calculate the device statistics data
in a specific time period such as average device downtime in the
specified time window. These statistics data can be further used to
detect wavelength division service anomaly or improve the accuracy
rate for wavelength division KPI anomaly detection. In this
scenario, we do not rely on the manual preconfigured threshold to
trigger alarm, instead, we automatically detect KPI anomaly in
advance and raise alarm, as seen in figure 1.
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+---------+ +----------+ +----------+ +--------+
| Network | | feature | | anomaly | | raise |
| data |+-->| selection+--->| detection|+-->| alarm |
+---------+ +----------+ +----------+ +--------+
Figure 1: anomaly detection
4.2. Risk assessment
In Data Analytics Engine, risk assessment is a component aiming at
providing an estimation of the overall network risk condition.
Unlike the anomaly detection component that copes with network faults
and failure that already happened, risk assessment module's goal is
to anticipant network event, forecast short term change and risk in
the network based on the trends of network data (e.g., fast growing,
fast dropping, slowly increasing, and slowly decreasing of KPI data).
This opens up a channel to reveal potential network problems or
locate the need for network optimization and upgrade.
Network KPIs provide fine-grained understanding of network
performance, which bring more value to network maintenance and
operation, including identifying possible bottlenecks, dimensioning
issues, and locating the need to perform network optimization. Based
on the various monitor mechanisms, if any high risk is occurred in
the network, administrators could be informed at a very early stage.
The ability to handle large amount of noisy KPI data properly is
vital to gain these desired insights.
Given hundreds of thousands of KPI data, it is a challenging issue to
assess network risk. Good network risk assessment criteria should be
indicative of local network-level problems, and hence be able to
provide prompt warnings and help locate potential problems when
trivial but persisting anomalies are observed. Meanwhile, it must
also describe system performance in a global sense by aggregating
multi-faceted information with large number of KPIs across the
network infrastructure. There are two strategies to design such KPI
network risk, as shown in figure 2:
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+---------+ +------------+ +------------+
| Network | | single KPI | | risk |
| data |+-->| scoring |+--->| assessment |
+---------+ +-----+------+ +------------+
| ^
| |
+-----v------+ |
| multi-KPI | |
| scoring +------------+
+------------+
Figure 2: risk assessment
1) Single KPI scoring: The scoring strategy for single KPI. In this
case, different dimensions of a KPI should be examined to score a
KPI;
2) Multi-KPI scoring: The scoring strategy for assessing the network
risk using values of many KPIs. If a device or a service is
monitored by several key KPIs, the risk should be analyzed by the
integration of these KPI scores.
5. Data Issues
5.1. Merge data from different time periods
In the process of data collection, the collection period of the same
KPI may be different from each other. For example, for a multi-
domain deployment service, there are many different collection
periods for network devices, such as 30s, 5min, 15min, and so on.
KPI data collected from different domains is need to be analyzed for
correlation. For example, anomaly detection of wavelength division
service data from different domains is performed, and comparison is
performed among different domains. So we need to merge data sets
from different periods into a integrated data set using metrics in
the period, such as mean value, peak value or media value. It then
raises a question that how these data sets are stored and assessed
with high efficiency.
6. Security Considerations
TBD.
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7. Conclusions
TBD.
8. Normative References
[I-D.ietf-wu-t2trg-network-telemetry]
Wu, Q., "Network Telemetry and Big Data Analysis", March
2016.
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", March 1997.
Authors' Addresses
Xiaojian Ding
Huawei
101 Software Avenue, Yuhua District
Nanjing, Jiangsu 210012
China
Email: dingxiaojian1@huawei.com
Will(Shucheng) Liu
Huawei
Bantian, Longgang District
Shenzhen 518129
P.R. China
Email: liushucheng@huawei.com
Chen Li
China Telecom
No.118 Xizhimennei street, Xicheng District
Beijing 100035
P.R. China
Email: lichen@ctbri.com.cn
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