网络诊断技术研究综述

doi:10.11959/j.issn.2096-6652.202316

摘要/Abstract

摘要：

网络诊断是一种高效且方便的通过端到端路径的性能测量来推断网络内部状态的方法，在有线和无线网络的链路测量和故障定位中得到了广泛应用。对网络诊断的4种基础模型布尔网络诊断技术、可加网络诊断技术、带宽网络诊断技术、随机网络诊断技术的模型基础进行了总结分析。系统梳理了网络诊断技术中的关键问题，主要包括监视器放置、信标服务放置、路径构建和数据分析、网络诊断中可识别性，以及不保证识别性时的网络诊断攻击等。同时梳理了基于网络编码的网络诊断、基于神经网络的网络诊断和网络功能虚拟化中节点故障定位等网络诊断领域的新研究和新问题。最后，基于网络诊断技术的发展现状，分析探讨了未来网络诊断技术的发展趋势。

关键词: 网络诊断, 网络测量, 监视器放置, 路径构建, 可识别性, 秩亏问题

Abstract:

Network tomography is an efficient and convenient tool to infer the internal state of a network through end-toend path measurement.It has been widely used in link measurement and fault location of wired and wireless networks.This paper summarized and analyzed the model foundation of four basic models of network tomography, boolean network tomography technology, additive network tomography technology, bandwidth network tomography technology and the stochastic network tomography technology.This paper systematically combed the key problems in network tomography technology, including monitor placement, beacon and service placement, path construction and data analysis, the problem of identifiability, and attack in network tomography when identification was not guaranteed.At the same time, the new research and new problems in the field of network tomography such as network tomography with network coding, neural network tomography and node fault location method in NFV were sorted out.Finally, based on the development status of network tomography technology, the future development trend of network tomography was analyzed and discussed.

Key words: network tomography, network measurement, monitor placement, path construction, identifiability, rank deficiency problem

中图分类号:

TP393

许晓佳,王永才,李德英. 网络诊断技术研究综述[J]. 智能科学与技术学报, 2023, 5(2): 163-179.

Xiaojia XU,Yongcai WANG,Deying LI. A survey on network tomography technology[J]. Chinese Journal of Intelligent Science and Technology, 2023, 5(2): 163-179.

图/表 13

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表2

可加网络诊断的监视器放置算法"

类型	测量路径	网络类型	研究问题	算法复杂度
MMP^[10]	无环路径	静态	放置最少数量的监视器，以识别所有链路度量	$O (\| V \| + \| L \|)$
GMMP^[6]	无环路径	静态	放置给定数量的监视器，以识别最大数量的链路度量	$O ({\| V \|}^{4} + {\| V \|}^{3} \| L \|)$
Scalpel^[40]	无环路径	静态	分配监视器，以识别所有感兴趣的链路	$O (\| V \| + \| L \|)$
OMA^[41]	无环路径	静态	分配最小数量的监视器，以识别所有感兴趣的链路	$O (\| V \| + \| L \|)$
MPK^[42]	无环路径	静态	放置监视器，使k-可识别链路的数量最大化	$O ({\| V \|}^{6})$
FMP^[43]	无环路径	动态	在拓扑变化的网络中放置最少监视器，识别链路度量	$O ({\| V \|}^{2})$

表2

表3

可加网络诊断的测量路径构建算法"

类型	测量路径	网络类型	研究问题	算法复杂度
CLIC^[51]	有环路径	静态	使用一个监视器和使用监控环路来识别链路度量的问题	$O ({\| V \|}^{2})$
CLR^[52]	有环路径	静态	使用测量节点识别链路度量并在它们之间建立路径的问题	$O ({\| V \|}^{2})$
STPC^[11]	无环路径	静态	构建可识别测量路径中单个链路度量的测量路径的问题	$O (\| V \| \| L \|)$
RoMe^[21]	无环路径	静态	在成本约束下最大化对已知故障的健壮性的路径选择问题	$O (2^{\| L \|} \| L \| {\| P \|}^{4})$
PC^[53]	无环路径	静态	选择能对网络全面监控覆盖的最小端到端测量路径的问题	$O (\| V \| (\| L \| + \| V \| \log (\| V \|)))$

表3

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类型	性能度量	适用问题
布尔网络诊断^[18]	将链路或节点故障指标等内部性能度量建模为未知的布尔常数	部分网络系统不需要精确掌握具体数值，只需要掌握设备状态，从端到端布尔测量中推断未知参数的二进制状态。
可加网络诊断^[11]	将平均链路延迟等可加的内部性能度量建模为未知常数	每个端到端测量等于对应测量路径上的链路度量的总和，从端到端的路径测量推断出单个链路的度量。
带宽网络诊断^[19]	将带宽这一内部性能度量建模为未知的常数	每个端到端测量等于对应测量路径上的链路的最小带宽，从端到端的路径测量推断出单个链路的带宽。
随机网络诊断^[20]	将瞬时链路延迟等内部性能度量建模为有未知分布的随机变量	每个端到端测量是从相应路径性能度量的分布中提取的随机实现。