An improved Particle Swarm Optimization (PSO) Force Unloading Algorithm for Multi-task and Multi-resource Moving Edge Computing Environment

Abstract

Abstract: In order to study the energy consumption of mobile devices in multi-resource complex environment, an improved particle swarm optimization (PSO) algorithm for unloading calculation of mobile edge devices is proposed. Firstly, a computing model is proposed based on the multi-environment energy consumption of mobile devices.Secondly, a fitness algorithm is designed to measure the advantages and disadvantages of resource allocation schemes for computing resource problems.Finally, an improved particle swarm optimization (PSO) algorithm for energy allocation is presented for solving the optimal solution to further reduce the energy consumption scheduling and allocation scheme of mobile devices.The comparison of energy consumption system response time and other indicators of mobile devices under various unloading strategies by simulation software shows that the proposed algorithm has a better performance in solving the optimal solution to reduce the energy consumption scheduling and allocation scheme of mobile devices on the premise of satisfying the user response time.

Key words: mobile edge computing, mobile edge devices, computation offloading, particle swarm optimization, fitness, energy consumption model, energy efficient

ZHANG Yan-hu, YAN Li-juan, MA Zhi-fen, ZHANG Yan-jun. An improved Particle Swarm Optimization (PSO) Force Unloading Algorithm for Multi-task and Multi-resource Moving Edge Computing Environment[J]. Computer and Modernization, 2022, 0(05): 54-60.

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An improved Particle Swarm Optimization (PSO) Force Unloading Algorithm for Multi-task and Multi-resource Moving Edge Computing Environment

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