Peer Offloading with Delayed Feedback in Fog Networks
2021-09-01
发表期刊IEEE INTERNET OF THINGS JOURNAL (IF:8.2[JCR-2023],9.0[5-Year])
ISSN2327-4662
EISSN2327-4662
卷号8期号:17页码:13690-13702
发表状态已发表
DOI10.1109/JIOT.2021.3067919
摘要

Comparing to cloud computing, fog computing performs computation and services at the edge of networks, thus relieving the computation burden of the data center and reducing the task latency of end devices. Computation latency is a crucial performance metric in fog computing, especially for real-time applications. In this article, we study a peer computation offloading problem for a fog network with unknown dynamics. In this scenario, each fog node (FN) can offload its computation tasks to neighboring FNs in a time slot manner. The offloading latency, however, could not be fed back to the task dispatcher instantaneously due to the uncertainty of the processing time in peer FNs. Besides, peer competition occurs when different FNs offload tasks to one FN at the same time. To tackle the above difficulties, we model the computation offloading problem as a sequential FN selection problem with delayed information feedback. Using the adversarial multiarm bandit framework, we construct an online learning policy to deal with delayed information feedback. Different contention resolution approaches are considered to resolve peer competition. Performance analysis shows that the regret of the proposed algorithm, or the performance loss with suboptimal FN selections, achieves a sublinear order, suggesting an optimal FN selection policy. Besides, we prove that the proposed strategy can result in a Nash equilibrium (NE) with all FNs playing the same policy. Simulation results validate the effectiveness of the proposed policy. © 2014 IEEE.

关键词Fog Computation burden Computation offloading Contention resolution Delayed information Performance analysis Performance metrices Real time application Selection problems Task analysis Computational modeling Peer-to-peer computing Internet of Things Real-time systems Heuristic algorithms Edge computing Adversarial multiarm bandit delayed feedback fog computing reinforcement learning task offloading
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收录类别SCI ; SCIE ; EI
语种英语
WOS研究方向Computer Science ; Engineering ; Telecommunications
WOS类目Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号WOS:000688231600046
出版者Institute of Electrical and Electronics Engineers Inc.
EI入藏号20211310151501
EI主题词Fog computing
EI分类号443.1 Atmospheric Properties
原始文献类型Journal article (JA)
来源库IEEE
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文献类型期刊论文
条目标识符https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/133225
专题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
作者单位
1.Chinese Academy of Sciences, Shanghai Advanced Research Institute, Shanghai, China
2.School of Information Science and Technology, ShanghaiTech University, Shanghai, China
3.Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland
4.Department of Applied Probability and Informatics, Peoples’ Friendship University of Russia (RUDN University), Moscow, Russia
5.Key Laboratory of Wireless Sensor Network & Communication, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China
推荐引用方式
GB/T 7714
Miao Yang,Hongbin Zhu,Hua Qian,et al. Peer Offloading with Delayed Feedback in Fog Networks[J]. IEEE INTERNET OF THINGS JOURNAL,2021,8(17):13690-13702.
APA Miao Yang,Hongbin Zhu,Hua Qian,Yevgeni Koucheryavy,Konstantin Samouylov,&Haifeng Wang.(2021).Peer Offloading with Delayed Feedback in Fog Networks.IEEE INTERNET OF THINGS JOURNAL,8(17),13690-13702.
MLA Miao Yang,et al."Peer Offloading with Delayed Feedback in Fog Networks".IEEE INTERNET OF THINGS JOURNAL 8.17(2021):13690-13702.
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