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A Dependency-Aware Task Offloading Strategy in Mobile Edge Computing Based on Improved NSGA-II

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Wireless Algorithms, Systems, and Applications (WASA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13473))

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Abstract

Rapid development of mobile communications has led to respectable latency-sensitive and computation-intensive mobile applications. There is a huge contradiction between high resource demands of these applications and limited resource of mobile devices. In this regard, mobile edge computing (MEC) is a promising technology, where computation tasks can be offloaded from mobile devices onto network edges with stronger capability. However, the dependency between tasks leads to high complexity for offloading decision. In this paper, we investigate the optimal offloading problem for completing dependency-aware tasks by minimizing the latency and energy cost. An improved non-dominated sorting genetic algorithm-II (INSGA-II) is proposed to solve this multiobjective problem. Simulation results validate the advantage of the proposed algorithm in terms of the performance of low latency and cost.

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Acknowledgement

This work was supported in part by the Fundamental Research Funds for the Central Universities under Grant 2022JBGP000 and in part by the National Natural Science Foundation of China under Grant 61931001.

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Authors and Affiliations

  1. School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China

    Chunyue Zhou, Mingxin Zhang, Qinghe Gao & Tao Jing

Authors
  1. Chunyue Zhou
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  2. Mingxin Zhang
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  3. Qinghe Gao
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  4. Tao Jing
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Corresponding author

Correspondence to Qinghe Gao .

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Editors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Lei Wang

  2. Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Michael Segal

  3. Chang Gung University, Taiwan, China

    Jenhui Chen

  4. Tianjin University, Tianjin, China

    Tie Qiu

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Zhou, C., Zhang, M., Gao, Q., Jing, T. (2022). A Dependency-Aware Task Offloading Strategy in Mobile Edge Computing Based on Improved NSGA-II. In: Wang, L., Segal, M., Chen, J., Qiu, T. (eds) Wireless Algorithms, Systems, and Applications. WASA 2022. Lecture Notes in Computer Science, vol 13473. Springer, Cham. https://doi.org/10.1007/978-3-031-19211-1_53

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  • DOI: https://doi.org/10.1007/978-3-031-19211-1_53

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-19210-4

  • Online ISBN: 978-3-031-19211-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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