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Computing Cost Optimization for Multi-BS in MEC by Offloading

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Abstract

As today’s Internet of Things (IoT) applications are becoming more complicated and intelligent, IoT devices alone can no longer well support the ever-increasing demand for powerful computation and high energy efficiency. Mobile Edge Computing (MEC) and 5G technology emerge as promising solutions, which enable IoT tasks to be offloaded to edge servers for effective processing. Though desirable, there however exists a mismatch between the massive IoT task workloads and limited wireless bandwidth, making it challenging to achieve an optimal offloading strategy at the mobile edge, e.g., the base station (BS) server. In this paper, we aim to migrate the most suitable offloading tasks to fully obtain the benefits of the MEC task offloading. We first formulate the task offloading model as an optimization problem, and theoretically prove the NP-hardness in achieving the optimal solution. Thus, a Genetic algorithm, named M-COGA, is proposed to solve the task offloading selection in both single and multiple BS scenarios. The algorithm focuses on offloading as many tasks as possible with the maximum cost offloading. The proposed cost function takes into account both the computation overhead and energy consumption. Besides, for the multi-BS coverage scenario, we also consider the approach flexibility as well as link load balance. And an enhanced dynamic task offloading scenario is further discussed. We verify the efficiency of our algorithm under the condition of both uniform and non-uniform distribution of covered nodes. Numerical experiments demonstrate that our dynamic allocating scheme can effectively work in MEC offloading. Besides, it largely outperforms the single BS scenarios and reduces the cost of edge devices.

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Acknowledgment

This research was supported by China Scholarship Council (CSC), Fund of Applied Basic Research Programs of Science and Technology Department (No.2018JY0290), F. Wang and J. Liu’s research is supported by an NSERC Discovery Grant, The authors also thank for the supporting of SINOPEC Key Laboratory of Geophysics and Graphic & image Collaborative Innovation Center of CUIT.

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Authors and Affiliations

  1. College of Communication Engineering, Chengdu University of Information Technology, Chengdu, China

    Wenzao Li & Yuwen Pan

  2. Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, Canada

    Fangxin Wang

  3. School of Computing Science, Simon Fraser University, Burnaby, Canada

    Wenzao Li, Fangxin Wang & Jiangchuan Liu

  4. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China

    Lei Zhang

  5. No.24 Block 1, Xuefu Road, Chengdu, China

    Wenzao Li

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  1. Wenzao Li
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Correspondence to Fangxin Wang.

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This is an expanded paper, and the previous version had been published in Qshine 2019.

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Li, W., Wang, F., Pan, Y. et al. Computing Cost Optimization for Multi-BS in MEC by Offloading. Mobile Netw Appl 27, 236–248 (2022). https://doi.org/10.1007/s11036-020-01627-y

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  • DOI: https://doi.org/10.1007/s11036-020-01627-y

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