Reliability-Aware Multi-Objective Memetic Algorithm for Workflow Scheduling Problem in Multi-Cloud System

• Published in: IEEE transactions on parallel and distributed systems Vol. 34; no. 4; pp. 1343 - 1361
• Main Authors: Qin, Shuo, Pi, Dechang, Shao, Zhongshi, Xu, Yue, Chen, Yang
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Amplification; Cloud computing; Costs; Critical path; Memetic algorithm; multi-cloud systems; multi-objective optimization; Multiple objective analysis; Operators; Optimization; Reliability; Resource utilization; Schedules; Scheduling; Task analysis; Virtual environments; workflow scheduling; Workflow software
• ISSN: 1045-9219; 1558-2183
• DOI: 10.1109/TPDS.2023.3245089

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.