Joint optimization of delay and energy consumption computation offloading scheme for MEC

被引：0
|
作者：
Yang H. [1 ]
Yang Z. [1 ,2 ]
Zhang X. [2 ]
Song Y. [1 ,2 ]
Dai Y. [2 ,3 ]
Huang C. [1 ,2 ]
Yue G. [1 ,2 ]
机构：
[1] Collegeof Science, Jiangxi University of Science and Technology, Ganzhou
[2] Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province, Jiaxing University, Jiaxing
[3] School of Computer Engineering and Science, Shanghai University, Shanghai
来源：
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2023年 / 29卷 / 07期
基金：
中国国家自然科学基金;
关键词：
computation offloading; delay and energy consumption; load balance; mobile edge computing; multi-mutation differential algorithm;
D O I：
10.13196/j.cims.2023.07.013
中图分类号：
学科分类号：
摘要：
In Mobile Edge Computing (MEC), computation offloading can effectively alleviate resource constraints and improve network quality of service. A joint optimization of delay and energy consumption computation offloading scheme for MEC was proposed with the joint optimization of task delay, terminal energy consumption and edge server load rate. A cost optimization model with multi-objective constraints was constructed, and a Multi-Mutation Differential Evolution (MDE) algorithm was designed by introducing the multi-mutation operator that was updated with iterative correlation probability to solve. To verify the effectiveness of MDE algorithm, three different scale experimental networks were constructed based on Autonomous Systems by Skitter public dataset. Compared with random computation offloading scheme, energy optimization computation offloading scheme and multi-objective greedy computation offloading scheme, MDE algorithm improved the execution success rate, offloading success rate and server load balancing by 13.23%, 12.96%, 29.37% respectively, which could realize efficient and stable computation offloading in MEC. © 2023 CIMS. All rights reserved.
引用
收藏
页码：2277 / 2291
页数：14
相关论文
共 18 条
  • [1] SHI Weisong, ZHANG Xingzhou, WANG Yifan, Et al., Edge computing: State-of-the-art and future directions, Journal of Computer Research and Development, 56, 1, pp. 69-89, (2019)
  • [2] DENG Xiaoheng, LI Dongsheng, WU Fan, 2018 edge computing topic, Journal of Computer Research and Development, 55, 3, pp. 447-448, (2018)
  • [3] TANG L, HE S B., Multi-user computation offloading in mobile edge computing: A behavioral perspective, IEEE Network, 32, 1, pp. 48-53, (2018)
  • [4] WU Y, QIAN L P, NI K J, Et al., Delay-minimization non-orthogonal multiple access enabled multi-user mobile edge computation offloading, IEEE Journal of Selected Topics in Signal Processing, 13, 3, pp. 392-407, (2019)
  • [5] RAHMAN G M S, DANG T, AHMED M., Deep reinforcement learning based computation offloading and resource allocation for low-latency fog radio access networks, Intelligent and Converged Networks, 1, 3, pp. 243-257, (2020)
  • [6] XU Z C, ZHAO L Q, LIANG W F, Et al., Energy-aware inference offloading for DNN-driven applications in mobile edge clouds, IEEE Transactions on Parallel and Distributed Systems, 32, 4, pp. 799-814, (2021)
  • [7] MA Huirong, CHEN Xu, ZHOU Zhi, Et al., Dynamic task offloading for mobile edge computing with green energy, Journal of Computer Research and Development, 57, 9, pp. 1823-1838, (2020)
  • [8] XU Jia, LI Xuejun, DING Ruimiao, Et al., Energy efficient multi-resource computation offloading strategy in mobile edge computing, Computer Integrated Manufacturing Systems, 25, 4, pp. 954-961, (2019)
  • [9] YANG T T, FENG H L, GAO S, Et al., Two-stage offloading optimization for energy-latency tradeoff with mobile edge computing in maritime internet of things, IEEE Internet of Things Journal, 7, 7, pp. 5954-5963, (2020)
  • [10] LIU Wei, HUANG Yucheng, DU Wei, Et al., Resource-constrained serial task offload strategy in mobile edge computing, Journal of Software, 31, 6, pp. 1889-1908, (2020)
12