Speed-Aware and Customized Task Offloading and Resource Allocation in Mobile Edge Computing

被引:8
|
作者
Zhu, Dali [1 ,2 ]
Li, Ting [1 ,2 ]
Tian, Hongfeng [3 ]
Yang, Yong [4 ]
Liu, Yinlong [1 ,2 ]
Liu, Haitao [1 ,2 ]
Geng, Liru [1 ]
Sun, Jiyan [1 ]
机构
[1] Chinese Acad Sci, Inst Informat Engn, Beijing 100864, Peoples R China
[2] Univ Chinese Acad Sci, Sch Cyber Secur, Beijing 100093, Peoples R China
[3] Publishing House Elect Ind, Beijing 100036, Peoples R China
[4] Beijing Univ Posts & Telecommun, Sch Informat & Commun Engn, Beijing 100876, Peoples R China
关键词
Task analysis; Delays; Quality of service; Wireless communication; Servers; Computational modeling; Resource management; Mobile edge computing; task offloading; deep reinforcement learning; customization; speed;
D O I
10.1109/LCOMM.2021.3075690
中图分类号
TN [电子技术、通信技术];
学科分类号
0809 ;
摘要
Recently, the task offloading and resource allocation (TORA) issues in mobile edge computing system have attracted much attention. However, the impact of different movement speeds on TORA problem and customized services for different service requirements have been largely ignored. These may severely influence the user's quality of service (QoS). Accordingly, a speed-aware and customized TORA scheme is proposed to minimize total service latency (consisted of execution delay and migration cost) for mobile users. First, the Advantage Actor Critic (A2C) algorithm is applied to dynamically decide on which computation node to execute the tasks. Second, we assign the user's priorities based on movement speeds and tolerable delay to allocate the wireless and computation resources respectively. Finally, the migration cost is introduced into objective model for further weakening the impact of service migration on user's QoS. Simulation results demonstrate the superiority and effectiveness of the proposed algorithm compared with the baselines.
引用
收藏
页码:2683 / 2687
页数:5
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