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Genetic Algorithm-Based Optimization of Offloading and Resource Allocation in Mobile-Edge Computing

by Zhi Li 1,2 and Qi Zhu 1,2,*
1
Jiangsu Key Laboratory of Wireless Communications, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
2
Engineering Research Center of Health Service System Based on Ubiquitous Wireless Networks, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
*
Author to whom correspondence should be addressed.
Information 2020, 11(2), 83; https://doi.org/10.3390/info11020083
Received: 20 December 2019 / Revised: 15 January 2020 / Accepted: 31 January 2020 / Published: 3 February 2020
Mobile edge computing (MEC) can use a wireless access network to serve smart devices nearby so as to improve the service experience of users. In this paper, a joint optimization method based on the Genetic Algorithm (GA) for task offloading proportion, channel bandwidth, and mobile edge servers’ (MES) computing resources is proposed in the scenario where some computing tasks can be partly offloaded to the MES. Under the limitation of wireless transmission resources and MESs’ processing resources, GA was used to solve the optimization problem of minimizing user task completion time, and the optimal offloading task strategy and resource allocation scheme were obtained. The simulation results show that the proposed algorithm can effectively reduce the task completion time and ensure the fairness of users’ completion times. View Full-Text
Keywords: mobile edge computing; offloading; genetic algorithm; resource allocation mobile edge computing; offloading; genetic algorithm; resource allocation
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Li, Z.; Zhu, Q. Genetic Algorithm-Based Optimization of Offloading and Resource Allocation in Mobile-Edge Computing. Information 2020, 11, 83.

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