Network Topology Inference Based on Subset Structure Fusion
Network topology measurement is an important component in network research. Network tomography is able to accurately infer network topology by using end-to-end measurement without cooperation of internal routers. Unfortunately, traditional network tomography methods can not accurately estimate topol...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9238011/ |
| _version_ | 1818349976764284928 |
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| author | Jian Ye Gaolei Fei Xuemeng Zhai Guangmin Hu |
| author_facet | Jian Ye Gaolei Fei Xuemeng Zhai Guangmin Hu |
| author_sort | Jian Ye |
| collection | DOAJ |
| description | Network topology measurement is an important component in network research. Network tomography is able to accurately infer network topology by using end-to-end measurement without cooperation of internal routers. Unfortunately, traditional network tomography methods can not accurately estimate topology in the non-stationary network due to the variability of traffic distribution. In this paper, we present a novel network topology inference method based on subset structure fusion for accurate topology inference in the non-stationary network. First, we propose an end-to-end measurement method named three-packet to accurately probe the three-leaf-nodes subset structures of the network without the assumption that the packet delay or loss follows a stable distribution. Second, we propose a metric for the shared path length based on the structural characteristics of the subset structures to fuse these subset structures into a correct complete topology. The analytical and simulation results show that our method is more applicable for topology inference in the non-stationary network compared with the existing methods. |
| first_indexed | 2024-12-13T18:14:31Z |
| format | Article |
| id | doaj.art-2914d7f94df54f3f89acd5ccacb33c96 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T18:14:31Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-2914d7f94df54f3f89acd5ccacb33c962022-12-21T23:35:53ZengIEEEIEEE Access2169-35362020-01-01819419219420510.1109/ACCESS.2020.30333319238011Network Topology Inference Based on Subset Structure FusionJian Ye0https://orcid.org/0000-0003-2762-9717Gaolei Fei1https://orcid.org/0000-0001-6529-3666Xuemeng Zhai2Guangmin Hu3School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaNetwork topology measurement is an important component in network research. Network tomography is able to accurately infer network topology by using end-to-end measurement without cooperation of internal routers. Unfortunately, traditional network tomography methods can not accurately estimate topology in the non-stationary network due to the variability of traffic distribution. In this paper, we present a novel network topology inference method based on subset structure fusion for accurate topology inference in the non-stationary network. First, we propose an end-to-end measurement method named three-packet to accurately probe the three-leaf-nodes subset structures of the network without the assumption that the packet delay or loss follows a stable distribution. Second, we propose a metric for the shared path length based on the structural characteristics of the subset structures to fuse these subset structures into a correct complete topology. The analytical and simulation results show that our method is more applicable for topology inference in the non-stationary network compared with the existing methods.https://ieeexplore.ieee.org/document/9238011/End-to-end measurementnetwork tomographynon-stationary networksubset structure fusiontopology inference |
| spellingShingle | Jian Ye Gaolei Fei Xuemeng Zhai Guangmin Hu Network Topology Inference Based on Subset Structure Fusion IEEE Access End-to-end measurement network tomography non-stationary network subset structure fusion topology inference |
| title | Network Topology Inference Based on Subset Structure Fusion |
| title_full | Network Topology Inference Based on Subset Structure Fusion |
| title_fullStr | Network Topology Inference Based on Subset Structure Fusion |
| title_full_unstemmed | Network Topology Inference Based on Subset Structure Fusion |
| title_short | Network Topology Inference Based on Subset Structure Fusion |
| title_sort | network topology inference based on subset structure fusion |
| topic | End-to-end measurement network tomography non-stationary network subset structure fusion topology inference |
| url | https://ieeexplore.ieee.org/document/9238011/ |
| work_keys_str_mv | AT jianye networktopologyinferencebasedonsubsetstructurefusion AT gaoleifei networktopologyinferencebasedonsubsetstructurefusion AT xuemengzhai networktopologyinferencebasedonsubsetstructurefusion AT guangminhu networktopologyinferencebasedonsubsetstructurefusion |