Multi-Objective Optimization Techniques in Cloud Task Scheduling: A Systematic Literature Review

Abstract

Task scheduling in cloud computing environment aims to identify alternative methods for effectively allocating competing cloud tasks to constrained resources, optimizing one or more objectives. This systematic literature review (SLR) examines advancements in multi-objective optimization techniques for cloud task scheduling from year 2010 to October 2024, providing an up-to-date analysis of the field. Cloud task scheduling, critical for optimizing performance, cost, and resource use, increasingly relies on multi-objective approaches to address complex and competing scheduling goals. This comprehensive review presents a detailed taxonomy and classification of multi-objective optimization methods, highlighting trends and developments across various approaches. Additionally, we conduct a comparative analysis of key scheduling objectives, testing environments, statistical evaluation methods, and datasets employed in recent studies, offering insights into current practices and best-fit approaches for different scenarios. The findings of this SLR aim to guide researchers and practitioners in selecting appropriate techniques, metrics, and datasets, supporting effective decision-making and advancing the design of cloud task scheduling systems.