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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Mobile edge computing (MEC) distributes computing and storage resources to the edge of the network closer to the user and significantly reduces user task completion latency and system energy consumption. This paper investigates the problem of computation offloading in a three-tier mobile edge computing network composed of multiple users, multiple edge servers, and a cloud server. In this network, each user’s task can be divided into multiple subtasks with serial and parallel priority relationships existing among these subtasks. An optimization model is established with the objective of minimizing the total user delay and processor cost under constraints such as the available resources of users and servers and the interrelationships among the subtasks. An improved gravitational search algorithm (IGSA) is proposed to solve this optimization model. In contrast with the other gravitational search algorithm, the convergence factor is introduced in the calculation of the resultant force and the crossover operation in a genetic algorithm is performed when generating the new particles during each iteration. The simulation results show that the proposed IGSA greatly improves the system performance compared with the existing algorithms.

Details

Title
An Improved Gravitational Search Algorithm for Task Offloading in a Mobile Edge Computing Network with Task Priority
Author
Xu, Ling 1   VIAFID ORCID Logo  ; Liu, Yunpeng 2   VIAFID ORCID Logo  ; Fan, Bing 3   VIAFID ORCID Logo  ; Xu, Xiaorong 1   VIAFID ORCID Logo  ; Mei, Yiguo 4   VIAFID ORCID Logo  ; Feng, Wei 1   VIAFID ORCID Logo 

 School of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, China; [email protected] (L.X.); [email protected] (X.X.) 
 Zhejiang Haikang Zhilian Technology Co., Ltd., Hangzhou 311113, China 
 Frontier Technology Service Center, Hangzhou Dianzi University, Hangzhou 310018, China; [email protected] 
 Huaxin Consulting Co., Ltd., Hangzhou 310051, China 
First page
540
Publication year
2024
Publication date
2024
Publisher
MDPI AG
e-ISSN
20799292
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2923907686
Copyright
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.