Toward Optimal Resource Allocation for Task Offloading in Mobile Edge Computing
Task offloading emerges as a promising solution in Mobile Edge Computing (MEC) scenarios to not only incorporate more processing capability but also save energy. There however exists a key conflict between the heavy processing workloads of terminals and the limited wireless bandwidth, making it challenging to determine the computing placement at the terminals or the remote servers. In this paper, we aim to migrate the most suitable offloading tasks to fully obtain the benefits from the resourceful cloud. The problem in this task offloading scenario is modeled as an optimization problem. Therefore, a Genetic Algorithm is then proposed to achieve maximal user selection and the most valuable task offloading. Specifically, the cloud is pondered to provide computing services for as many edge wireless terminals as possible under the limited wireless channels. The base stations (BSs) serve as the edge for task coordination. The tasks are jointly considered to minimize the computing overhead and energy consumption, where the cost model of local devices is used as one of the optimization objectives in this wireless mobile selective schedule. We also establish the multi-devices task offloading scenario to further verify the efficiency of the proposed allocating schedule. Our extensive numerical experiments demonstrate that our allocating scheme can effectively take advantage of the cloud server and reduce the cost of end users.
KeywordsMobile edge computing Task offloading Genetic algorithm Computing overhead Allocating schedule
This research was supported by China Scholarship Council (CSC), Fund of Applied Basic Research Programs of Science and Technology Department (No. 2018JY0290). The work of Lei Zhang was supported in part by the National Natural Science Foundation of China under Grant 61902257. The work of Fangxin Wang and Jiangchuan Liu is supported by a Canada NSERC Discovery Grant.
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