Computing Cost Optimization for Multi-BS in MEC by Offloading
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Computing Cost Optimization for Multi-BS in MEC by Offloading Wenzao Li1,2,5 · Fangxin Wang2,3 · Yuwen Pan1 · Lei Zhang4 · Jiangchuan Liu2
© Springer Science+Business Media, LLC, part of Springer Nature 2020
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. Keywords Mobile edge computing · Task offloading · Genetic algorithm · Computing overhead · Allocating schedule
1 Introduction 1.1 Background and motivation The mobile Internet and Internet of Things (IoT) applications [1–4] are experiencing an explosive growth in This is an expanded paper, and the previous version had been published in Qshine 2019. Fangxin Wang
[email protected] Wenzao Li [email protected] 1
College of Communication Engineering, Chengdu University of Information Technology, Chengdu, China
2
School of Computing Science, Simon Fraser University, Burnaby, Canada
3
Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, Canada
4
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
5
No.24 Block 1, Xuefu Road, Chengdu, China
recent years, calling for much higher computation capacity for intelligent processing. Mobile Edge Computing (MEC) emerging as a promising computing allocation scheme [5] enables the interconnected devices to complete many complicated and computation-intensive tasks. Yet, on
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