Energy-Efficient Offloading for Mobile Edge Computing in 5G Heterogeneous Networks

Ke Zhang; Yuming Mao; Supeng Leng; Quanxin Zhao; Longjiang Li; Xin Peng; Li Pan; Sabita Maharjan; Yan Zhang

doi:10.1109/ACCESS.2016.2597169

IEEE Access (Jan 2016)

Energy-Efficient Offloading for Mobile Edge Computing in 5G Heterogeneous Networks

Ke Zhang,
Yuming Mao,
Supeng Leng,
Quanxin Zhao,
Longjiang Li,
Xin Peng,
Li Pan,
Sabita Maharjan,
Yan Zhang

Affiliations

Ke Zhang: School of Communication and Information Engineering, University of Electronic Science and Technology of China, Chengdu, China
Yuming Mao: School of Communication and Information Engineering, University of Electronic Science and Technology of China, Chengdu, China
Supeng Leng: School of Communication and Information Engineering, University of Electronic Science and Technology of China, Chengdu, China
Quanxin Zhao: School of Communication and Information Engineering, University of Electronic Science and Technology of China, Chengdu, China
Longjiang Li: School of Communication and Information Engineering, University of Electronic Science and Technology of China, Chengdu, China
Xin Peng: College of Information and Communication Engineering, Hunan Institute of Science and Technology, Yueyang, China
Li Pan: College of Information and Communication Engineering, Hunan Institute of Science and Technology, Yueyang, China
Sabita Maharjan: Simula Research Laboratory, Oslo, Norway
Yan Zhang: ORCiD; Simula Research Laboratory, Oslo, Norway

DOI: https://doi.org/10.1109/ACCESS.2016.2597169
Journal volume & issue: Vol. 4
pp. 5896 – 5907

Abstract

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Mobile edge computing (MEC) is a promising paradigm to provide cloud-computing capabilities in close proximity to mobile devices in fifth-generation (5G) networks. In this paper, we study energy-efficient computation offloading (EECO) mechanisms for MEC in 5G heterogeneous networks. We formulate an optimization problem to minimize the energy consumption of the offloading system, where the energy cost of both task computing and file transmission are taken into consideration. Incorporating the multi-access characteristics of the 5G heterogeneous network, we then design an EECO scheme, which jointly optimizes offloading and radio resource allocation to obtain the minimal energy consumption under the latency constraints. Numerical results demonstrate energy efficiency improvement of our proposed EECO scheme.

Published in IEEE Access

ISSN: 2169-3536 (Online)
Publisher: IEEE
Country of publisher: United States
LCC subjects: Technology: Electrical engineering. Electronics. Nuclear engineering
Website: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639

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