计算机科学
云计算
工作流程
分布式计算
调度(生产过程)
作业车间调度
备份
模因算法
进化算法
地铁列车时刻表
数学优化
人工智能
数据库
数学
操作系统
作者
Shuo Qin,Dechang Pi,Zhongshi Shao,Yaoxian Xu,Yang Chen
出处
期刊:IEEE Transactions on Parallel and Distributed Systems
[Institute of Electrical and Electronics Engineers]
日期:2023-04-01
卷期号:34 (4): 1343-1361
被引量:8
标识
DOI:10.1109/tpds.2023.3245089
摘要
With the development of cloud computing, multi-cloud systems have become common platforms for hosting and executing workflow applications in recent years. However, the complexity of workflow scheduling increases exponentially because of the diversified billing mechanisms, heterogeneous virtual machines, and reliability of multi-cloud systems. This article focuses on a multi-objective workflow scheduling problem in multi-cloud systems (MOWSP-MCS). The makespan, cost, and reliability are considered the optimization objectives from the perspective of users. Compared with the classical multi-objective workflow scheduling in the cloud environment, MOWSP-MCS allows users to apply the backup technique to improve reliability. To solve the MOWSP-MCS, this article proposes a reliability-aware multi-objective memetic algorithm (RA-MOMA) containing a diversification strategy and intensification strategy. In the diversification strategy, several problem-specific genetic operators are introduced to construct the diversified offspring individuals. In the intensification strategy, four problem-specific neighborhood operators are designed based on the critical path and resource utilization rate to improve the quality of the individuals in the archive set. A comprehensive numerical experiment is conducted to evaluate the effectiveness of RA-MOMA. The comparisons with several related algorithms demonstrate the superiority of RA-MOMA for solving the MOWSP-MCS.
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