Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/105523
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Computing | - |
dc.creator | Xie, K | - |
dc.creator | Chen, Y | - |
dc.creator | Wang, X | - |
dc.creator | Xie, G | - |
dc.creator | Cao, J | - |
dc.creator | Wen, J | - |
dc.creator | Yang, G | - |
dc.creator | Sun, J | - |
dc.date.accessioned | 2024-04-15T07:34:50Z | - |
dc.date.available | 2024-04-15T07:34:50Z | - |
dc.identifier.issn | 1063-6692 | - |
dc.identifier.uri | http://hdl.handle.net/10397/105523 | - |
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 et al., "Accurate and Fast Recovery of Network Monitoring Data With GPU-Accelerated Tensor Completion," in IEEE/ACM Transactions on Networking, vol. 28, no. 4, pp. 1601-1614, Aug. 2020 is available at https://doi.org/10.1109/TNET.2020.2987845. | en_US |
dc.subject | Graphics Processing Unit (GPU) | en_US |
dc.subject | Parallel tensor completion | en_US |
dc.subject | Recovery of network monitoring data | en_US |
dc.title | Accurate and fast recovery of network monitoring data with GPU-accelerated tensor completion | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 1601 | - |
dc.identifier.epage | 1614 | - |
dc.identifier.volume | 28 | - |
dc.identifier.issue | 4 | - |
dc.identifier.doi | 10.1109/TNET.2020.2987845 | - |
dcterms.abstract | Monitoring the performance of a large network would involve a high measurement cost. To reduce the overhead, sparse network monitoring techniques may be applied to select paths or time intervals to take the measurements, while the remaining monitoring data can be inferred leveraging the spatial-temporal correlations among data. The quality of missing data recovery, however, highly relies on the specific inference technique adopted. Tensor completion is a promising technique for more accurate missing data inference by exploiting the multi-dimensional data structure. However, data processing for higher dimensional tensors involves a large amount of computation, which prevents conventional tensor completion algorithms from practical application in the presence of large amount of data. This work takes the initiative to investigate the potential and methodologies of performing parallel processing for high-speed and high accuracy tensor completion over Graphics Processing Units (GPUs). We propose a GPU-accelerated parallel Tensor Completion scheme (GPU-TC) for accurate and fast recovery of missing data. To improve the data recovery accuracy and speed, we propose three novel techniques to well exploit the tensor factorization structure and the GPU features: grid-based tensor partition, independent task assignment based on Fisher-Yates shuffle, sphere facilitated and memory-correlated scheduling. We have conducted extensive experiments using network traffic trace data to compare the proposed GPU-TC with the state of art tensor completion algorithms and matrix-based algorithms. The experimental results demonstrate that GPU-TC can achieve significantly better performance in terms of two relative error ratio metrics and computation time. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | IEEE/ACM transactions on networking, Aug. 2020, v. 28, no. 4, p. 1601-1614 | - |
dcterms.isPartOf | IEEE/ACM transactions on networking | - |
dcterms.issued | 2020-08 | - |
dc.identifier.scopus | 2-s2.0-85090768885 | - |
dc.identifier.eissn | 1558-2566 | - |
dc.description.validate | 202402 bcch | - |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | COMP-0258 | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | National Natural Science Foundation of China; Hunan Provincial Natural Science Foundation of China; U.S. NSF; Open Project Funding of State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences; CERNET Innovation Project; Open Foundation of State key Laboratory of Networking and Switching Technology (Beijing University of Posts and Telecommunications); Peng Cheng Laboratory Project of Guangdong Province | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 43659770 | en_US |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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Cao_Accurate_Fast_Recovery.pdf | Pre-Published version | 8.55 MB | Adobe PDF | View/Open |
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