文献类型：期刊文献

英文题名：Energy, Cost and Reliability-Aware Workflow Scheduling on Multi-Cloud Systems: A Multi-Objective Evolutionary Approach

作者：Fang, Zhuoyue[1,2];Yu, Huiqun[1,2];Fan, Guisheng[1,2];Li, Zengpeng[1,2];Zhang, Jiayin[1,2]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China;[2]Shanghai Engn Res Ctr Smart Energy, Shanghai 201103, Peoples R China

年份：2025

卷号：22

期号：5

起止页码：4788

外文期刊名：IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT

收录：;EI(收录号：20252818777139);WOS:【SCI-EXPANDED(收录号:WOS:001590953600025)】；

基金：This work was partially supported by the NSF of China under grants No. 62372174 and No. 62276097, and Research Project Funding of Shanghai Data Exchange Corporation.

语种：英文

外文关键词：Scheduling; Costs; Reliability; Cloud computing; Scheduling algorithms; Energy consumption; Heuristic algorithms; Evolutionary computation; Virtual machines; Optimization; Multi-objective optimization; workflow scheduling; multi-cloud systems; evolutionary algorithm

摘要：Nowadays, cloud computing has become a suitable platform for hosting and executing workflow applications. As the diversity and scale of these applications continue to increase, single-cloud environments are becoming insufficient to meet users' requirements. Instead, multi-cloud environments have emerged as an ideal solution. However, the complexity of workflow scheduling in multi-cloud environments increases significantly due to the diversified billing mechanisms, heightened reliability demands, and the requirements for reducing energy consumption. To address these challenges, this paper proposes a multi-objective evolutionary algorithm called ECRWSM for workflow scheduling on multi-cloud systems. First, ECRWSM utilizes the population initialization strategy to generate a population with excellent uniformity and sufficient randomness. Then, the diversification strategy is employed to thoroughly explore the solution space. Next, the individual enhancement strategy is used to further improve the solutions. Additionally, an external archive is maintained to store non-dominated solutions throughout the evolutionary process. Comprehensive experiments are conducted to validate the performance of ECRWSM. The experimental results demonstrate that our proposed algorithm ECRWSM outperforms both classical and recent scheduling algorithms.