Multi-Objective Task and Workflow Scheduling Approaches in Cloud Computing: a Comprehensive Review

• Published in: Journal of grid computing Vol. 18; no. 3; pp. 327 - 356
• Main Authors: Hosseinzadeh, Mehdi, Ghafour, Marwan Yassin, Hama, Hawkar Kamaran, Vo, Bay, Khoshnevis, Afsane
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.09.2020
• Subjects: Computer Science; Management of Computing and Information Systems; Processor Architectures; User Interfaces and Human Computer Interaction; NSGA-II; Pareto front; Task; Energy; Cloud; Workflow; Scheduling; MOEA; Optimization
• ISSN: 1570-7873; 1572-9184
• DOI: 10.1007/s10723-020-09533-z

Efficient task and workflow scheduling are very important for improving resource management and reducing power consumption in cloud computing data centers (DCs). However, regarding numerous tasks, virtual machines, and several objectives which should be taken into account, scheduling is considered to be an NP-Hard problem. Multi-objective optimization is an interesting technique to deal with multiple conflicting goals which have been utilized by various schemes to solve the task and workflow scheduling problems. This paper focuses on the metaheuristic multi-objective optimization context and presents a comprehensive survey and overview of the multi-objective scheduling approaches designed for various cloud computing environments. It classifies the scheduling schemes regarding their applied multi-objective optimization algorithms and describes how they have adapted the optimization algorithms to solve scheduling problems. Furthermore, a comparison of the multi-objective scheduling schemes is provided, which illuminates future research directions, and finally concluding remarks are presented.