Low cost network traffic measurement and fast recovery via redundant row subspace-based matrix completion
Traffic matrices (TMs) are essential for managing networks. Getting the whole TMs is difficult because of the high measurement cost. Several recent studies propose sparse measurement schemes to reduce the cost, which involve taking measurements on only a subset of origin and destination pairs (OD pa...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-12-01
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| Series: | Connection Science |
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| Online Access: | http://dx.doi.org/10.1080/09540091.2023.2218069 |
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| _version_ | 1849471783569195008 |
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| author | Kai Jin Kun Xie Jiazheng Tian Wei Liang Jigang Wen |
| author_facet | Kai Jin Kun Xie Jiazheng Tian Wei Liang Jigang Wen |
| author_sort | Kai Jin |
| collection | DOAJ |
| description | Traffic matrices (TMs) are essential for managing networks. Getting the whole TMs is difficult because of the high measurement cost. Several recent studies propose sparse measurement schemes to reduce the cost, which involve taking measurements on only a subset of origin and destination pairs (OD pairs) and inferring data for unmeasured OD pairs through matrix completion. However, existing sparse network measurement schemes suffer from the problems of high computation costs and low recovery quality. This paper investigates the coherence feature of real traffic flow data traces (Abilene and GÈANT). Both data sets are high coherence, with column coherence greater than row coherence. According to the coherence feature of both data sets, we propose our Redundant Row Subspace-based Matrix Completion (RRS-MC). RRS-MC involves several techniques. Firstly, we design an algorithm to identify subspace rows (OD pairs) from historical data. Secondly, based on the identified subspace rows, we design our sampling scheduling algorithm, which takes full measurement samples in subspace rows while taking partial measurement samples in the remaining rows. Moreover, we propose a redundant sampling rule prevent the recovery accuracy decrease caused by the subspace rows varying. Finally, we design a completion algorithm to recover the partially measured rows. We conduct extensive experiments. Results indicate that the proposed scheme is superior to the state-of-the-art sampling and completion scheme in computation costs and recovery accuracy. |
| format | Article |
| id | doaj-art-e6eda358e1c84210aeec5f1db4fbc2d1 |
| institution | Kabale University |
| issn | 0954-0091 1360-0494 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj-art-e6eda358e1c84210aeec5f1db4fbc2d12025-08-20T03:24:43ZengTaylor & Francis GroupConnection Science0954-00911360-04942023-12-0135110.1080/09540091.2023.22180692218069Low cost network traffic measurement and fast recovery via redundant row subspace-based matrix completionKai Jin0Kun Xie1Jiazheng Tian2Wei Liang3Jigang Wen4Hunan UniversityHunan UniversityHunan UniversityHunan University of Science and TechnologyHunan UniversityTraffic matrices (TMs) are essential for managing networks. Getting the whole TMs is difficult because of the high measurement cost. Several recent studies propose sparse measurement schemes to reduce the cost, which involve taking measurements on only a subset of origin and destination pairs (OD pairs) and inferring data for unmeasured OD pairs through matrix completion. However, existing sparse network measurement schemes suffer from the problems of high computation costs and low recovery quality. This paper investigates the coherence feature of real traffic flow data traces (Abilene and GÈANT). Both data sets are high coherence, with column coherence greater than row coherence. According to the coherence feature of both data sets, we propose our Redundant Row Subspace-based Matrix Completion (RRS-MC). RRS-MC involves several techniques. Firstly, we design an algorithm to identify subspace rows (OD pairs) from historical data. Secondly, based on the identified subspace rows, we design our sampling scheduling algorithm, which takes full measurement samples in subspace rows while taking partial measurement samples in the remaining rows. Moreover, we propose a redundant sampling rule prevent the recovery accuracy decrease caused by the subspace rows varying. Finally, we design a completion algorithm to recover the partially measured rows. We conduct extensive experiments. Results indicate that the proposed scheme is superior to the state-of-the-art sampling and completion scheme in computation costs and recovery accuracy.http://dx.doi.org/10.1080/09540091.2023.2218069sparse measurement techniquehigh column coherencesubspace-based matrix completion |
| spellingShingle | Kai Jin Kun Xie Jiazheng Tian Wei Liang Jigang Wen Low cost network traffic measurement and fast recovery via redundant row subspace-based matrix completion Connection Science sparse measurement technique high column coherence subspace-based matrix completion |
| title | Low cost network traffic measurement and fast recovery via redundant row subspace-based matrix completion |
| title_full | Low cost network traffic measurement and fast recovery via redundant row subspace-based matrix completion |
| title_fullStr | Low cost network traffic measurement and fast recovery via redundant row subspace-based matrix completion |
| title_full_unstemmed | Low cost network traffic measurement and fast recovery via redundant row subspace-based matrix completion |
| title_short | Low cost network traffic measurement and fast recovery via redundant row subspace-based matrix completion |
| title_sort | low cost network traffic measurement and fast recovery via redundant row subspace based matrix completion |
| topic | sparse measurement technique high column coherence subspace-based matrix completion |
| url | http://dx.doi.org/10.1080/09540091.2023.2218069 |
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