General Identifiability Condition for Network Topology Monitoring with Network Tomography
Accurate knowledge of network topology is vital for network monitoring and management. Network tomography can probe the underlying topologies of the intervening networks solely by sending and receiving packets between end hosts: the performance correlations of the end-to-end paths between each pair...
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Online Access: | https://www.mdpi.com/1424-8220/19/19/4125 |
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doaj-8140023f609e43f881be6c7c41b2e6e82020-11-24T21:56:43ZengMDPI AGSensors1424-82202019-09-011919412510.3390/s19194125s19194125General Identifiability Condition for Network Topology Monitoring with Network TomographyShengli Pan0Zongwang Zhang1Zhiyong Zhang2Deze Zeng3Rui Xu4Zhihong Rao5Hubei Key Laboratory of Intelligent Geo-Information Processing, School of Computer Science, China University of Geosciences, Wuhan 430078, ChinaHubei Key Laboratory of Intelligent Geo-Information Processing, School of Computer Science, China University of Geosciences, Wuhan 430078, ChinaCyberspace Security Key Laboratory of Sichuan Province & Cyberspace Security Technology Laboratory of CETC, China Electronic Technology Cyber Security Co. LTD., Chengdu 610041, ChinaHubei Key Laboratory of Intelligent Geo-Information Processing, School of Computer Science, China University of Geosciences, Wuhan 430078, ChinaCyberspace Security Key Laboratory of Sichuan Province & Cyberspace Security Technology Laboratory of CETC, China Electronic Technology Cyber Security Co. LTD., Chengdu 610041, ChinaCyberspace Security Key Laboratory of Sichuan Province & Cyberspace Security Technology Laboratory of CETC, China Electronic Technology Cyber Security Co. LTD., Chengdu 610041, ChinaAccurate knowledge of network topology is vital for network monitoring and management. Network tomography can probe the underlying topologies of the intervening networks solely by sending and receiving packets between end hosts: the performance correlations of the end-to-end paths between each pair of end hosts can be mapped to the lengths of their shared paths, which could be further used to identify the interior nodes and links. However, such performance correlations are usually heavily affected by the time-varying cross-traffic, making it hard to keep the estimated lengths consistent during different measurement periods, i.e., once inconsistent measurements are collected, a biased inference of the network topology then will be yielded. In this paper, we prove conditions under which it is sufficient to identify the network topology accurately against the time-varying cross-traffic. Our insight is that even though the estimated length of the shared path between two paths might be “zoomed in or out” by the cross-traffic, the network topology can still be recovered faithfully as long as we obtain the relative lengths of the shared paths between any three paths accurately.https://www.mdpi.com/1424-8220/19/19/4125network monitoringnetwork tomographyend-to-end measurementtopology identifiability |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Shengli Pan Zongwang Zhang Zhiyong Zhang Deze Zeng Rui Xu Zhihong Rao |
spellingShingle |
Shengli Pan Zongwang Zhang Zhiyong Zhang Deze Zeng Rui Xu Zhihong Rao General Identifiability Condition for Network Topology Monitoring with Network Tomography Sensors network monitoring network tomography end-to-end measurement topology identifiability |
author_facet |
Shengli Pan Zongwang Zhang Zhiyong Zhang Deze Zeng Rui Xu Zhihong Rao |
author_sort |
Shengli Pan |
title |
General Identifiability Condition for Network Topology Monitoring with Network Tomography |
title_short |
General Identifiability Condition for Network Topology Monitoring with Network Tomography |
title_full |
General Identifiability Condition for Network Topology Monitoring with Network Tomography |
title_fullStr |
General Identifiability Condition for Network Topology Monitoring with Network Tomography |
title_full_unstemmed |
General Identifiability Condition for Network Topology Monitoring with Network Tomography |
title_sort |
general identifiability condition for network topology monitoring with network tomography |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2019-09-01 |
description |
Accurate knowledge of network topology is vital for network monitoring and management. Network tomography can probe the underlying topologies of the intervening networks solely by sending and receiving packets between end hosts: the performance correlations of the end-to-end paths between each pair of end hosts can be mapped to the lengths of their shared paths, which could be further used to identify the interior nodes and links. However, such performance correlations are usually heavily affected by the time-varying cross-traffic, making it hard to keep the estimated lengths consistent during different measurement periods, i.e., once inconsistent measurements are collected, a biased inference of the network topology then will be yielded. In this paper, we prove conditions under which it is sufficient to identify the network topology accurately against the time-varying cross-traffic. Our insight is that even though the estimated length of the shared path between two paths might be “zoomed in or out” by the cross-traffic, the network topology can still be recovered faithfully as long as we obtain the relative lengths of the shared paths between any three paths accurately. |
topic |
network monitoring network tomography end-to-end measurement topology identifiability |
url |
https://www.mdpi.com/1424-8220/19/19/4125 |
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