Open Access

ARTICLE

A Hybrid Heuristic Service Caching and Task Offloading Method for Mobile Edge Computing

by Yongxuan Sang, Jiangpo Wei^*, Zhifeng Zhang, Bo Wang

Software Engineering College, Zhengzhou University of Light Industry, Zhengzhou, 450001, China

* Corresponding Author: Jiangpo Wei. Email:

Computers, Materials & Continua 2023, 76(2), 2483-2502. https://doi.org/10.32604/cmc.2023.040485

Received 20 March 2023; Accepted 09 June 2023; Issue published 30 August 2023

Abstract

Computing-intensive and latency-sensitive user requests pose significant challenges to traditional cloud computing. In response to these challenges, mobile edge computing (MEC) has emerged as a new paradigm that extends the computational, caching, and communication capabilities of cloud computing. By caching certain services on edge nodes, computational support can be provided for requests that are offloaded to the edges. However, previous studies on task offloading have generally not considered the impact of caching mechanisms and the cache space occupied by services. This oversight can lead to problems, such as high delays in task executions and invalidation of offloading decisions. To optimize task response time and ensure the availability of task offloading decisions, we investigate a task offloading method that considers caching mechanism. First, we incorporate the cache information of MEC into the model of task offloading and reduce the task offloading problem as a mixed integer nonlinear programming (MINLP) problem. Then, we propose an integer particle swarm optimization and improved genetic algorithm (IPSO_IGA) to solve the MINLP. IPSO_IGA exploits the evolutionary framework of particle swarm optimization. And it uses a crossover operator to update the positions of particles and an improved mutation operator to maintain the diversity of particles. Finally, extensive simulation experiments are conducted to evaluate the performance of the proposed algorithm. The experimental results demonstrate that IPSO_IGA can save 20% to 82% of the task completion time, compared with state-of-the-art and classical algorithms. Moreover, IPSO_IGA is suitable for scenarios with complex network structures and computing-intensive tasks.

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Keywords

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