Research Article
An Ant Colony Optimization Fuzzy Clustering Task Scheduling Algorithm in Mobile Edge Computing
@INPROCEEDINGS{10.1007/978-3-030-21373-2_51, author={Jianwei Liu and Xianglin Wei and Tongxiang Wang and Junwei Wang}, title={An Ant Colony Optimization Fuzzy Clustering Task Scheduling Algorithm in Mobile Edge Computing}, proceedings={Security and Privacy in New Computing Environments. Second EAI International Conference, SPNCE 2019, Tianjin, China, April 13--14, 2019, Proceedings}, proceedings_a={SPNCE}, year={2019}, month={6}, keywords={Mobile edge computing Task scheduling Ant colony optimization algorithm Fuzzy clustering algorithm}, doi={10.1007/978-3-030-21373-2_51} }- Jianwei Liu
Xianglin Wei
Tongxiang Wang
Junwei Wang
Year: 2019
An Ant Colony Optimization Fuzzy Clustering Task Scheduling Algorithm in Mobile Edge Computing
SPNCE
Springer
DOI: 10.1007/978-3-030-21373-2_51
Abstract
Mobile edge computing has always been a key issue in the development of the mobile Internet and the Internet of things, how to efficiently schedule tasks has gradually become the focus of mobile edge computing research. Task scheduling problem belongs to the NP-hard optimization problem. Many traditional heuristic algorithms are applied to deal with the task scheduling problem. For improving the problem that ant colony algorithm has slow convergence speed, an ant colony optimization fuzzy clustering algorithm is proposed in this paper. In this algorithm, the fuzzy clustering algorithm is used to reduce the search space range in order to reduce the complexity of the scheduling algorithm and the number of iterations. And the optimal solution of the scheduling is found using the strong global search ability of ant colony algorithm. The simulation results show that the performance of the ant colony optimization fuzzy clustering algorithm is better than that of the First-Come-First-Served algorithm and the traditional ant colony optimization algorithm.