A Delay and Energy Tradeoff Optimization Algorithm for Task Offloading in Mobile-Edge Computing Networks

doi:10.13190/j.jbupt.2019-093

Journal of Beijing University of Posts and Telecommunications ›› 2020, Vol. 43 ›› Issue (2): 110-115.doi: 10.13190/j.jbupt.2019-093

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A Delay and Energy Tradeoff Optimization Algorithm for Task Offloading in Mobile-Edge Computing Networks

JING Ze-wei¹, YANG Qing-hai¹, QIN Meng²

1. State Key Laboratory of Integrated Service Networks, Xidian University, Xi'an 710071, China;
2. Peng Cheng Laboratory, Shenzhen 518055, China

Received:2019-05-28 Published:2020-04-28

Abstract

Abstract: In order to enhance the task offloading utility in mobile-edge computing (MEC) networks,a delay and energy tradeoff optimization algorithm was proposed for maximizing the users' task offloading gains. The original optimization problem was decomposed into two sub-problems,i.e.,the joint transmit power and sub-channel allocation sub-problem and the MEC computing frequency allocation sub-problem,upon the analysis of the restriction of communication and computation resources to the delay and energy consumption performances. The optimal MEC computing frequency was directly derived in the closed form by the Karush-Kuhn-Tucker condition. In addition,an efficient bisection method based transmit power allocation algorithm and a bipartite graph matching based sub-channel allocation algorithm were proposed,respectively. Numerical simulation results showed that,the proposed algorithm could improve the task offloading utility remarkably when compared with some traditional algorithms.

Key words: mobile-edge computing, task offloading, sub-channel allocation, energy consumption, task completion time

CLC Number:

TP393

JING Ze-wei, YANG Qing-hai, QIN Meng. A Delay and Energy Tradeoff Optimization Algorithm for Task Offloading in Mobile-Edge Computing Networks[J]. Journal of Beijing University of Posts and Telecommunications, 2020, 43(2): 110-115.

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A Delay and Energy Tradeoff Optimization Algorithm for Task Offloading in Mobile-Edge Computing Networks

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