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An Optimized Greedy-Based Task Offloading Method for Mobile Edge Computing

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Algorithms and Architectures for Parallel Processing (ICA3PP 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 13155))

Abstract

With the development of smart mobile devices (SMDs), computationally intensive and latency-sensitive applications are emerging. However, Mobile devices have limited processing power by nature. To overcome this problem, mobile edge computing enables users to offload tasks to proximal edge servers for faster task computation. Most studies in task offloading consider stable systems and ignore the number of tasks fluctuating over time. Poor offloading decisions will overload edge servers during peak periods, which leads to significantly high latency. To address this challenge, an optimized greedy-based offloading method (OGOM) is designed to offload tasks. OGOM adopts different offloading strategies depending on the server load factor. When edge servers are highly loaded, OGOM offloads some of the tasks to more idle servers instead of the servers with the lowest theoretical latency to achieve load balancing. Simulation results show that the OGOM is effective in avoiding edge server overload. In addition, OGOM reduces latency by an average of 20% compared to the normal greedy-based offloading method.

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Authors and Affiliations

  1. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China

    Wei Zhou, Chuangwei Lin, Jirun Duan, Ke Ren & Wanchun Dou

  2. Department of Computing, Macquarie University, Sydney, Australia

    Xuyun Zhang

Authors
  1. Wei Zhou
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  2. Chuangwei Lin
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  3. Jirun Duan
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  4. Ke Ren
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  5. Xuyun Zhang
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  6. Wanchun Dou
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Corresponding author

Correspondence to Wanchun Dou .

Editor information

Editors and Affiliations

  1. Xiamen University, Xiamen, China

    Yongxuan Lai

  2. Beijing Normal University, Zhuhai, China

    Tian Wang

  3. Xiamen University, Xiamen, China

    Min Jiang

  4. Tianjin University, Tianjin, China

    Guangquan Xu

  5. Hunan University, Changsha, China

    Wei Liang

  6. University of Naples Parthenope, Naples, Italy

    Aniello Castiglione

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Zhou, W., Lin, C., Duan, J., Ren, K., Zhang, X., Dou, W. (2022). An Optimized Greedy-Based Task Offloading Method for Mobile Edge Computing. In: Lai, Y., Wang, T., Jiang, M., Xu, G., Liang, W., Castiglione, A. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2021. Lecture Notes in Computer Science(), vol 13155. Springer, Cham. https://doi.org/10.1007/978-3-030-95384-3_31

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  • DOI: https://doi.org/10.1007/978-3-030-95384-3_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-95383-6

  • Online ISBN: 978-3-030-95384-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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