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Energy-Aware and Mobility-Driven Computation Offloading in MEC

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Abstract

Heuristic algorithms are widely used in Mobile Edge Computing(MEC) to improve the performance of mobile devices. However, the time complexity of the heuristic algorithm is high, and it is complex to optimize under constraints. Therefore, we proposed Multi-User Energy Constraint Time Optimization Algorithm(MU-ECTOA) for workflow makespan optimization under energy constraints. MU-ECTOA includes three stages: cluster analysis, evaluation of performance, and workflow offloading. In the first stage, the workflow tasks are classified according to their characteristics; In the second stage, the subtask groups are obtained, and the evaluation results of each subtask group are obtained. In the third stage, the optimal subtask group is selected for offloading and then updated the ready time of edge nodes. Extensive experiments have been conducted, the ACO&GA, Max-Min, Particle Swarm Optimization(PSO), GA-DPSO, and SFLA are taken as the compared methods. The results of MU-ECTOA performs better in aspects in completion time, load balancing, and successful offloading rate compared with other methods. By comparing the results of algorithms, the makespans of the algorithms are close, but the algorithm complexity and load balancing of MU-ECTOA are much better; The time complexity of the MU-ECTOA algorithm is close to the Max-Min’s, but MU-ECTOA performs better in makespan and algorithm reliability.

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Data Availability

The datasets supporting the conclusions of this article are included in Characterization of scientific workflows [DOI: https://doi.org/10.1109/WORKS.2008.4723958].

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Acknowledgements

This research is supported by the Capacity building project of local universities Science and Technology Commission of Shanghai Municipality No.22010504100, the Shanghai Rising-Star Program (Sailing Program) No. 22YF1448100, the Scientific Research Starting Foundation of Shanghai Institute of Technology No.YJ2021-53, Development Fund for young and middle-aged scientific and technological talents of Shanghai Institute of Technology under grants No. ZQ2021-19, Development of Science and Technology of Shanghai Institute of Technology under grants No.KJFZ2021-176, KJFZ2021-177.

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  1. Department of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai, China

    Liqiong Chen, Yingda Liu, Yijun Lu & Huaiying Sun

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  1. Liqiong Chen
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Chen, L., Liu, Y., Lu, Y. et al. Energy-Aware and Mobility-Driven Computation Offloading in MEC. J Grid Computing 21, 26 (2023). https://doi.org/10.1007/s10723-023-09654-1

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