Game Theory-based Algorithm for Multi-server Multi-user Video Analysis Task Offloading

被引:0
|
作者
Chen Y. [1 ]
Zhang S. [1 ]
Jin Y.-B. [1 ]
Qian Z.-Z. [1 ]
Lu S.-L. [1 ]
机构
[1] State Key Laboratory for Novel Software Technology (Nanjing University), Nanjing
来源
Ruan Jian Xue Bao/Journal of Software | 2023年 / 34卷 / 12期
关键词
edge computing; game theory; Nash equilibrium; task offloading; video analysis;
D O I
10.13328/j.cnki.jos.006745
中图分类号
学科分类号
摘要
In the past decade or so, artificial intelligence-related services and applications have boomed, and they require high computing power, high bandwidth, and low latency. Edge computing is currently regarded as one of the most appropriate solutions for such applications, especially for video analysis-related ones. This study investigates multi-server multi-user heterogeneous video analysis task offloading, where users select appropriate edge servers and then upload their raw video data to the servers for video analysis. It models the issue of multi-server multi-user heterogeneous video analysis task offloading as a multiplayer game issue. The aim is to effectively deal with the competition for and sharing of the limited network resources among the numerous users and achieve a stable network resource allocation situation where each user has no incentive to change their task offloading decision unilaterally. With the optimization goal of minimizing the overall delay, this study successively investigates the non-distributed and distributed video analysis scenarios and proposes the game theory-based algorithms of potential optimal server selection and video unit allocation accordingly. Rigorous mathematical proof reveals that Nash equilibrium can be reached by the proposed algorithms in both of the two cases, and a low overall delay is guaranteed. Finally, extensive experiments on actual datasets show that the proposed methods reduce the overall delay by 26.3% on average, compared with that of other currently available algorithms. © 2023 Chinese Academy of Sciences. All rights reserved.
引用
收藏
页码:5940 / 5956
页数:16
相关论文
共 42 条
  • [1] Li ZY, Wang Q, Chen YF, Xie GQ, Li RF., A survey on task offloading research in vehicular edge computing, Chinese Journal of Computers, 44, 5, pp. 963-982, (2021)
  • [2] Zhou YZ, Zhang D., Near-end cloud computing: Opportunities and challenges in the post-cloud computing era, Chinese Journal of Computers, 42, 4, pp. 677-700, (2019)
  • [3] Shi WS, Zhang XZ, Wang YF, Zhang QY., Edge Computing: State-of-the-art and future directions, Journal of Computer Research and Development, 56, 1, pp. 69-89, (2019)
  • [4] Xie RC, Lian XF, Jia QM, Huang T, Liu YJ., Survey on computation offloading in mobile edge computing, Journal on Communications, 39, 11, pp. 138-155, (2018)
  • [5] Wang S, Zhang X, Zhang Y, Wang L, Yang JW, Wang WB., A survey on mobile edge networks: Convergence of computing, caching and communications, IEEE Access, 5, pp. 6757-6779, (2017)
  • [6] Caprolu M, Di Pietro R, Lombardi F, Raponi S., Edge computing perspectives: Architectures, technologies, and open security issues, Proc. of the 2019 IEEE Int’l Conf. on Edge Computing (EDGE), pp. 116-123, (2019)
  • [7] Zhou Z, Chen X, Li E, Zeng LK, Luo K, Zhang JS., Edge intelligence: Paving the last mile of artificial intelligence with edge computing, Proc. of the IEEE, 107, 8, pp. 1738-1762, (2019)
  • [8] Hsieh K, Ananthanarayanan G, Bodik P, Venkataraman S, Bahl P, Philipose M, Gibbons B, Mutlu O., Focus: Querying large video datasets with low latency and low cost, Proc. of the 13th USENIX Conf. on Operating Systems Design and Implementation, pp. 269-286, (2018)
  • [9] Satyanarayanan M, Bahl P, Caceres R, Davies N., The case for VM-based cloudlets in mobile computing, IEEE Pervasive Computing, 8, 4, pp. 14-23, (2009)
  • [10] Ananthanarayanan G, Bahl P, Bodik P, Chintalapudi K, Philipose M, Ravindranath L, Sinha S., Real-time video analytics: The killer App for edge computing, Computer, 50, 10, pp. 58-67, (2017)