ONLINE LEARNING FOR COMPUTATION PEER OFFLOADING WITH SEMI-BANDIT FEEDBACK
Zhn, Hongbin1,2; Kang, Kai3; Luo, Xiliang1; Qian, Hua3
2019
Source Publication2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)
Volume2019-May
Pages4524-4528
Status已发表
DOI10.1109/ICASSP.2019.8682398
AbstractFog computing is emerging as a promising paradigm to perform distributed, low-latency computation. Efficient computation peer offloading is critical to fully utilize the computational resources in fog networks. In this paper, we consider computation peer of-floading problem in a fog network with time-varying stochastic time of arrival tasks and channel conditions. Such time-varying conditions are not available to all fog nodes. In order to minimize the latency of accomplishing arrival tasks, we propose an online algorithm based on combinatorial upper confidence bounds algorithm with two uncertain variables under the non-stationary bandit model. The proposed computation offloading policy is optimized based on historical feedback. The performance of the proposed scheme is validated through numerical simulations.
KeywordFog Computing Computation Peer Offloading Online Learning Combinatorial Multi-Armed Bandit (CMAB)
Conference PlaceBrighton, United kingdom
Conference Date12-17 May 2019
URL查看原文
Indexed ByEI ; CPCI
Language英语
Funding ProjectScience and Technology Commission Foundation of Shanghai[18511103502]
WOS IDWOS:000482554004152
PublisherIEEE
EI Accession Number20192907201036
WOS KeywordRESOURCE ; ALLOCATION
Original Document TypeProceedings Paper
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type会议论文
Identifierhttps://kms.shanghaitech.edu.cn/handle/2MSLDSTB/34286
Collection信息科学与技术学院_博士生
信息科学与技术学院_特聘教授组_钱骅组
信息科学与技术学院_PI研究组_罗喜良组
Affiliation1.School of Information Science and Technology, ShanghaiTech University, Shanghai, China
2.Shanghai Institute of Microsystem and Information Technology, CAS, Shanghai, China
3.Shanghai Advanced Research Institute, CAS, Shanghai, China
First Author AffilicationSchool of Information Science and Technology
First Signature AffilicationSchool of Information Science and Technology
Recommended Citation
GB/T 7714
Zhn, Hongbin,Kang, Kai,Luo, Xiliang,et al. ONLINE LEARNING FOR COMPUTATION PEER OFFLOADING WITH SEMI-BANDIT FEEDBACK[C]:IEEE,2019:4524-4528.
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