Research Article
Toward Optimal Resource Allocation for Task Offloading in Mobile Edge Computing
@INPROCEEDINGS{10.1007/978-3-030-38819-5_4, author={Wenzao Li and Yuwen Pan and Fangxing Wang and Lei Zhang and Jiangchuan Liu}, title={Toward Optimal Resource Allocation for Task Offloading in Mobile Edge Computing}, proceedings={Quality, Reliability, Security and Robustness in Heterogeneous Systems. 15th EAI International Conference, QShine 2019, Shenzhen, China, November 22--23, 2019, Proceedings}, proceedings_a={QSHINE}, year={2020}, month={1}, keywords={Mobile edge computing Task offloading Genetic algorithm Computing overhead Allocating schedule}, doi={10.1007/978-3-030-38819-5_4} }- Wenzao Li
Yuwen Pan
Fangxing Wang
Lei Zhang
Jiangchuan Liu
Year: 2020
Toward Optimal Resource Allocation for Task Offloading in Mobile Edge Computing
QSHINE
Springer
DOI: 10.1007/978-3-030-38819-5_4
Abstract
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.