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Optimal Offloading Decision Strategies and Their Influence Analysis of Mobile Edge Computing

College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2019, 19(14), 3231;
Received: 27 June 2019 / Revised: 19 July 2019 / Accepted: 19 July 2019 / Published: 23 July 2019
Mobile edge computing (MEC) has become more popular both in academia and industry. Currently, with the help of edge servers and cloud servers, it is one of the substantial technologies to overcome the latency between cloud server and wireless device, computation capability and storage shortage of wireless devices. In mobile edge computing, wireless devices take responsibility with input data. At the same time, edge servers and cloud servers take charge of computation and storage. However, until now, how to balance the power consumption of edge devices and time delay has not been well addressed in mobile edge computing. In this paper, we focus on strategies of the task offloading decision and the influence analysis of offloading decisions on different environments. Firstly, we propose a system model considering both energy consumption and time delay and formulate it into an optimization problem. Then, we employ two algorithms—Enumerating and Branch-and-Bound—to get the optimal or near-optimal decision for minimizing the system cost including the time delay and energy consumption. Furthermore, we compare the performance between two algorithms and draw the conclusion that the comprehensive performance of Branch-and-Bound algorithm is better than that of the other. Finally, we analyse the influence factors of optimal offloading decisions and the minimum cost in detail by changing key parameters. View Full-Text
Keywords: MEC; computation offloading; optimal offloading decision MEC; computation offloading; optimal offloading decision
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Xu, J.; Hao, Z.; Sun, X. Optimal Offloading Decision Strategies and Their Influence Analysis of Mobile Edge Computing. Sensors 2019, 19, 3231.

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