计算机科学 ›› 2023, Vol. 50 ›› Issue (10): 275-281.doi: 10.11896/jsjkx.220900185
张俊娜1,2, 陈家伟1, 鲍想1, 刘春红1, 袁培燕1
ZHANG Junna1,2, CHEN Jiawei1, BAO Xiang1, LIU Chunhong1, YUAN Peiyan1
摘要: 边缘计算通过在网络边缘侧提供更优的计算和存储能力,能够有效降低用户设备的执行时延和能耗。随着应用程序对计算和存储资源的需求越来越大,任务卸载作为消除用户设备固有限制的一种有效手段,成为了主要的研究热点之一。然而,在已有的任务卸载研究中,常常忽略不同类型的任务对服务需求的多样性以及边缘服务器服务缓存有限的情形,从而导致不可行的卸载决策。因此,在服务缓存约束下,研究了能够使得用户设备执行成本最优的任务卸载问题。首先设计了云服务器、边缘服务器和本地设备的协同卸载模型,用于平衡边缘服务器的负载问题,同时借助云服务器弥补边缘服务器有限的服务缓存能力。然后,提出了适用于云边端协同的任务卸载算法,优化用户设备的执行成本。当任务被卸载时,先采用改进的贪婪算法选择最佳的边缘服务器,再通过比较任务在不同位置上的执行成本,来确定任务的卸载决策。实验结果表明,所提算法相比对比算法能够有效降低用户设备的执行成本。
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[1]ALI S,ZHAO H P,KIM H.Mobile edge computing:A promi-sing paradigm for future communication systems[C]//Procee-dings of the 2018 IEEE Region 10 Conference.Jeju,Korea,2018:1183-1187. [2]ZHANG L,CHAI R,YANG T,et al.Min-max worst-case design for computation offloading in multi-user MEC system[C]//IEEE Conference on Computer Communications Workshops(INFOCOM 2020).Beijing,China,2020:1075-1080. [3]ZHANG Y L,LIANG Y Z,YIN M J,et al.Survey on the Me-thods of Computation Offloading in Mobile Edge Computing[J].Chinese Journal of Computers,2021,44(12):2406-2430. [4]ABBAS N,ZHANG Y,TACHERKORDI A,et al.Mobile edge computing:A survey[J].IEEE Internet of Things Journal,2017,5(1):450-465. [5]ZHANG H B,LI H,CHEN S X,et al.Computing Offloading and Resource Optimization in Ultra-dense Networks with Mobile Edge Computation[J].Journal of Electronics & Information Technology,2019,41(5):1194-1201. [6]ZHANG N,GUO S,DONG Y,et al.Joint task offloading and data caching in mobile edge computing networks[J].Computer Networks,2020,182:107446. [7]REN J,GAO L,YU J L,et al.Energy-Efficient Deep Learning Task Scheduling Strategy for Edge Device[J].Chinese Journal of Computers,2020,43(3):440-452. [8]WANG H,LIN Z,LV T.Energy and Delay Minimization of Partial Computing Offloading for D2D-Assisted MEC Systems[C]//Proceedings of the 2021 IEEE Wireless Communications and Networking Conference.Nanjing,China,2021:1-6. [9]CHEN L,WU J,ZHANG J,et al.Dependency-Aware Computation Offloading for Mobile Edge Computing with Edge-Cloud Cooperation[J].IEEE Transactions on Cloud Computing,2020,10(4):2451-2468. [10]NING Z,DONG P,KONG X,et al.A cooperative partial compu-tation offloading scheme for mobile edge computing enabled Internet of Things[J].IEEE Internet of Things Journal,2018,6(3):4804-4814. [11]ALFAKIH T,HASSAN M M,GUMAEI A,et al.Task offloa-ding and resource allocation for mobile edge computing by deep reinforcement learning based on SARSA[J].IEEE Access,2020,8:54074-54084. [12]MAO Y,YOU C,ZHANG J,et al.Mobile edge computing:Survey and research outlook[J].arXiv:1701.01090,2017. [13]ZHANG X,LI Z,LAI C,et al.Joint Edge Server Placement and Service Placement in Mobile Edge Computing[J].IEEE Internet of Things Journal,2021,9(13):11261-11274. [14]BI S,HUANG L,ZHANG Y J A.Joint optimization of service caching placement and computation offloading in mobile edge computing systems[J].IEEE Transactions on Wireless Communications,2020,19(7):4947-4963. [15]GUO M,HUANG X,WANG W,et al.HAGP:A Heuristic Algorithm Based on Greedy Policy for Task Offloading with Reliability of MDs in MEC of the Industrial Internet[J].Sensors,2021,21(10):35 |
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