A–ESD: Auxiliary Edge-Server Deployment for Load Balancing in Mobile Edge Computing

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Vydáno v:Mathematics vol. 13, no. 19 (2025), p. 3087-3108
Hlavní autor: Niu Sen
Další autoři: Zhang, Xuewei, Wang, Simin, Liao Kaili, Zhang Bofeng, Zou Guobing
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MDPI AG
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100 1 |a Niu Sen  |u School of Computer and Information Engineering, Institute for Artificial Intelligence, Shanghai Polytechnic University, Shanghai 201209, China; niusen@sspu.edu.cn (S.N.); xwzhang@sspu.edu.cn (X.Z.); smwang@sspu.edu.cn (S.W.); bfzhang@sspu.edu.cn (B.Z.) 
245 1 |a A–ESD: Auxiliary Edge-Server Deployment for Load Balancing in Mobile Edge Computing 
260 |b MDPI AG  |c 2025 
513 |a Journal Article 
520 3 |a In recent years, the deployment of edge servers has attracted significant research interest, with a focus on maximizing their utilization under resource constraint to improve overall efficiency. However, most existing studies concentrate on initial deployment strategies, paying limited attention to approaches involving incremental expansion. As user demands continue to escalate, many edge systems are facing overload situations that hinder their ability to meet performance requirements. To tackle these challenges, this paper introduces an auxiliary edge-server deployment strategy designed to achieve load balancing across edge systems and alleviate local server overloads. The problem is herein referred to as the Auxiliary Edge Server Deployment (A–ESD) problem, and the aim is to determine the optimal deployment scheme for auxiliary edge servers. A–ESD is modeled as a multi-objective optimization problem subject to global constraints and is demonstrated to be NP-hard. An enhanced genetic algorithm called LBA–GA is proposed to efficiently solve the A–ESD problem. The algorithm is designed to maximize overall load balance while minimizing total system delay. Extensive experiments conducted on real-world datasets demonstrate that LBA–GA outperforms existing methods, delivering superior load balancing, reduced latency, and higher cost-effectiveness. 
653 |a Expansion 
653 |a User experience 
653 |a Genetic algorithms 
653 |a Servers 
653 |a Edge computing 
653 |a Communication 
653 |a Optimization 
653 |a Mobile computing 
653 |a Overloading 
653 |a Multiple objective analysis 
653 |a Cloud computing 
653 |a Performance evaluation 
653 |a Constraints 
653 |a Energy consumption 
653 |a Internet of Things 
653 |a Load balancing 
653 |a Cost effectiveness 
700 1 |a Zhang, Xuewei  |u School of Computer and Information Engineering, Institute for Artificial Intelligence, Shanghai Polytechnic University, Shanghai 201209, China; niusen@sspu.edu.cn (S.N.); xwzhang@sspu.edu.cn (X.Z.); smwang@sspu.edu.cn (S.W.); bfzhang@sspu.edu.cn (B.Z.) 
700 1 |a Wang, Simin  |u School of Computer and Information Engineering, Institute for Artificial Intelligence, Shanghai Polytechnic University, Shanghai 201209, China; niusen@sspu.edu.cn (S.N.); xwzhang@sspu.edu.cn (X.Z.); smwang@sspu.edu.cn (S.W.); bfzhang@sspu.edu.cn (B.Z.) 
700 1 |a Liao Kaili  |u School of Computer and Information Engineering, Institute for Artificial Intelligence, Shanghai Polytechnic University, Shanghai 201209, China; niusen@sspu.edu.cn (S.N.); xwzhang@sspu.edu.cn (X.Z.); smwang@sspu.edu.cn (S.W.); bfzhang@sspu.edu.cn (B.Z.) 
700 1 |a Zhang Bofeng  |u School of Computer and Information Engineering, Institute for Artificial Intelligence, Shanghai Polytechnic University, Shanghai 201209, China; niusen@sspu.edu.cn (S.N.); xwzhang@sspu.edu.cn (X.Z.); smwang@sspu.edu.cn (S.W.); bfzhang@sspu.edu.cn (B.Z.) 
700 1 |a Zou Guobing  |u School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China; gbzou@shu.edu.cn 
773 0 |t Mathematics  |g vol. 13, no. 19 (2025), p. 3087-3108 
786 0 |d ProQuest  |t Engineering Database 
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