Scholarlink

<p>Efficient composition of cloud services under Quality of Service (QoS) constraints is a critical challenge in service-oriented computing. Traditional metaheuristic approaches often struggle to balance global exploration and SLA compliance in high-dimensional, dynamic environments. To address this, we propose an Enhanced Prairie Dog Optimization (EPDO) algorithm that integrates Lévy flight dynamics with behaviorally adaptive search phases. The EPDO is validated through benchmark functions and real-world service composition situations and exhibits a 94.1% SLA conformity rate, which is better than baseline algorithms GA, PSO, MFO, and original PDO. It also demonstrates a 15–25% speedup in convergence and maintains similar runtime efficiency. As demonstrated by the findings, EPDO’s future prospects are in its capable, constraint-aware optimization in extremely massive-scale cloud service systems.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A novel quality of service-aware service composition method for cloud computing using enhanced Prairie Dog Metaheuristic Optimization Algorithm

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月1日

查看全文
  • Dongge Tian

摘要

Efficient composition of cloud services under Quality of Service (QoS) constraints is a critical challenge in service-oriented computing. Traditional metaheuristic approaches often struggle to balance global exploration and SLA compliance in high-dimensional, dynamic environments. To address this, we propose an Enhanced Prairie Dog Optimization (EPDO) algorithm that integrates Lévy flight dynamics with behaviorally adaptive search phases. The EPDO is validated through benchmark functions and real-world service composition situations and exhibits a 94.1% SLA conformity rate, which is better than baseline algorithms GA, PSO, MFO, and original PDO. It also demonstrates a 15–25% speedup in convergence and maintains similar runtime efficiency. As demonstrated by the findings, EPDO’s future prospects are in its capable, constraint-aware optimization in extremely massive-scale cloud service systems.