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Efficiently Inferring Top-k Largest Monitoring Data Entries Based on Discrete Tensor Completion

文献类型：期刊论文

作者Tian, Jiazheng6; Xie, Kun6; Wang, Xin5; Xie, Gaogang3,4; Li, Kenli6; Wen, Jigang2; Zhang, Dafang6; Cao, Jiannong1
刊名IEEE-ACM TRANSACTIONS ON NETWORKING
出版日期2021-12-01
卷号29期号:6页码:2737-2750
关键词Top-k largest entry inference tensor completion
ISSN号1063-6692
DOI10.1109/TNET.2021.3103527
英文摘要Network-wide monitoring is important for many network functions. Due to the need of sampling to reduce high measurement cost, system failure, and unavoidable data transmission loss, network monitoring systems suffer from the incompleteness of network monitoring data. Different from the traditional network monitoring data estimation problem which aims to infer all missing monitoring data entries with incomplete measurement data, we study a challenging problem of inferring the top-k largest monitoring data entries. The recent study shows it is promising to more accurately interpolate the missing data with a 3-D tensor compared to that based on a 2-D matrix. Taking full advantage of the multilinear structures, we apply tensor completion to first recover the missing data and then find the top-k data entries. To reduce the computational overhead, we propose a novel discrete tensor completion model which uses binary codes to represent the factor matrices. Based on the model, we further propose three novel techniques to speed up the whole top-k entry inference process: a discrete optimization algorithm to train the binary factor matrices, bit operations to facilitate quick missing data inference, and simplifying the finding of topk largest entries with binary code partition. In our discrete tensor completion model, only one bit is needed to represent the entry in the factor matrices instead of a real value (32 bits) needed in traditional tensor completion model, thus the storage cost is reduced significantly. To quickly infer the top-k largest data entries when measurement data arrive sequentially, we also propose a sliding window based online algorithm using the discrete tensor completion model. Extensive experiments using five real data sets and one synthetic data set demonstrate that compared with the state of art tensor completion algorithms, our discrete tensor completion algorithm can achieve similar top-k entry inference accuracy using significantly smaller time and storage space.
资助项目National Natural Science Foundation of China[62025201] ; National Natural Science Foundation of China[61972144] ; National Natural Science Foundation of China[61976087] ; China Education and Research Network (CRENET) Innovation Project[NGII20190118] ; Hunan Provincial Innovation Foundation for Postgraduate[CX20190305] ; NSF Electrical, Communications and Cyber Systems (ECCS)[1731238] ; NSF Electrical, Communications and Cyber Systems (ECCS)[2030063] ; NSF Communication and Information Foundations (CIF)[2007313]
WOS研究方向Computer Science ; Engineering ; Telecommunications
语种英语
WOS记录号WOS:000731147300029
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/17952]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Xie, Kun
作者单位1.Hong Kong Polytech Univ, Dept Comp, Hong Kong, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China
3.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100190, Peoples R China
4.Chinese Acad Sci, Comp Network Informat Ctr, Beijing 100190, Peoples R China
5.SUNY Stony Brook, Dept Elect & Comp Engn, Stony Brook, NY 11794 USA
6.Hunan Univ, Coll Comp Sci & Elect Engn, Changsha 410082, Peoples R China
推荐引用方式
GB/T 7714		 Tian, Jiazheng,Xie, Kun,Wang, Xin,et al. Efficiently Inferring Top-k Largest Monitoring Data Entries Based on Discrete Tensor Completion[J]. IEEE-ACM TRANSACTIONS ON NETWORKING,2021,29(6):2737-2750.
APA Tian, Jiazheng.,Xie, Kun.,Wang, Xin.,Xie, Gaogang.,Li, Kenli.,...&Cao, Jiannong.(2021).Efficiently Inferring Top-k Largest Monitoring Data Entries Based on Discrete Tensor Completion.IEEE-ACM TRANSACTIONS ON NETWORKING,29(6),2737-2750.
MLA Tian, Jiazheng,et al."Efficiently Inferring Top-k Largest Monitoring Data Entries Based on Discrete Tensor Completion".IEEE-ACM TRANSACTIONS ON NETWORKING 29.6(2021):2737-2750.

入库方式： OAI收割

来源：计算技术研究所

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