改进多种群进化算法求解移动边缘计算中任务调度问题

doi:10.12052/gdutxb.220010

Abstract

Abstract: Mobile edge computing (MEC) can provide users with low-latency network services and cloud-like computing services by deploying servers at the edge network which is close to users. Mobile devices (MDs) offload their tasks to edge servers for computing via the network access points, which can effectively reduce the power consumption of MDs and the completion time of their tasks. However, users have to pay for communications when they offload their tasks to edge servers. A MEC system is studied which contains multiple users and multiple edge computing nodes. Mathematical models are built for task completion time, power consumption, and communication cost of MDs, and the problem is formulated to minimize these objectives. A task scheduling algorithm based on a multi-population evolutionary algorithm is proposed to solve this problem. The scheduling algorithm minimizes the comprehensive cost of MDs by optimizing the offloading decisions and resource allocation decisions for MDs. Lots of simulations are conducted to verify that the proposed algorithm can reduce the comprehensive cost of MDs better compared with other scheduling algorithms.

Key words: mobile edge computing, task scheduling, multi-population evolutionary algorithm

CLC Number:

TP393

Zhu Qing-hua, Lu An-bang, Zhou Jian-tie, Hou Yan. An Improved Multi-population Evolutionary Algorithm for Task Scheduling in a Mobile Edge Computing Environment[J].Journal of Guangdong University of Technology, 2022, 39(04): 9-16.

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