Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/105536
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Computing | - |
dc.creator | Xie, K | en_US |
dc.creator | Chen, Y | en_US |
dc.creator | Wang, X | en_US |
dc.creator | Xie, G | en_US |
dc.creator | Cao, J | en_US |
dc.creator | Wen, J | en_US |
dc.date.accessioned | 2024-04-15T07:34:54Z | - |
dc.date.available | 2024-04-15T07:34:54Z | - |
dc.identifier.issn | 1063-6692 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/105536 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | ©2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
dc.rights | The following publication K. Xie, Y. Chen, X. Wang, G. Xie, J. Cao and J. Wen, "Accurate and Fast Recovery of Network Monitoring Data: A GPU Accelerated Matrix Completion," in IEEE/ACM Transactions on Networking, vol. 28, no. 3, pp. 958-971, June 2020 is available at https://doi.org/10.1109/TNET.2020.2976129. | en_US |
dc.subject | GPU | en_US |
dc.subject | Locality-sensitive hash | en_US |
dc.subject | Parallel matrix completion | en_US |
dc.title | Accurate and fast recovery of network monitoring data : a GPU accelerated matrix completion | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 958 | en_US |
dc.identifier.epage | 971 | en_US |
dc.identifier.volume | 28 | en_US |
dc.identifier.issue | 3 | en_US |
dc.identifier.doi | 10.1109/TNET.2020.2976129 | en_US |
dcterms.abstract | Gaining a full knowledge of end-to-end network performance is important for some advanced network management and services. Although it becomes increasingly critical, end-to-end network monitoring usually needs active probing of the path and the overhead will increase quadratically with the number of network nodes. To reduce the measurement overhead, matrix completion is proposed recently to predict the end-to-end network performance among all node pairs by only measuring a small set of paths. Despite its potential, applying matrix completion to recover the missing data suffers from low recovery accuracy and long recovery time. To address the issues, we propose MC-GPU to exploit Graphics Processing Units (GPUs) to enable parallel matrix factorization for high-speed and highly accurate Matrix Completion. To well exploit the special architecture features of GPUs for both task independent and data-independent parallel task execution, we propose several novel techniques: similar OD (origin and destination) pairs reordering taking advantage of the locality-sensitive hash (LSH) functions, balanced matrix partition, and parallel matrix completion. We implement the proposed MC-GPU on the GPU platform and evaluate the performance using real trace data. We compare the proposed MC-GPU with the state of the art matrix completion algorithms, and our results demonstrate that MC-GPU can achieve significantly faster speed with high data recovery accuracy. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | IEEE/ACM transactions on networking, June 2020, v. 28, no. 3, p. 958-971 | en_US |
dcterms.isPartOf | IEEE/ACM transactions on networking | en_US |
dcterms.issued | 2020-06 | - |
dc.identifier.scopus | 2-s2.0-85086903615 | - |
dc.identifier.eissn | 1558-2566 | en_US |
dc.description.validate | 202402 bcch | - |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | COMP-0315 | - |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | National Natural Science Foundation of China; Hunan Provincial Natural Science Foundation of Chin; U.S. NSF; Open Project Funding of State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences; Peng Cheng Laboratory Project of Guangdong Province | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 43661740 | - |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Cao_Accurate_Fast_Recovery.pdf | Pre-Published version | 10.81 MB | Adobe PDF | View/Open |
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