Journal Article10.1109/access.2025.3529839
Multi-Objective Optimization Techniques in Cloud Task Scheduling: A Systematic Literature Review
Olanrewaju L. Abraham,Md Asri Ngadi,Johan Bin Mohamad Sharif,Mohd Kufaisal Mohd Sidik +3 more
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TL;DR: This systematic literature review (2010-2024) examines advancements in multi-objective optimization techniques for cloud task scheduling, presenting a taxonomy and classification of methods, trends, and developments to guide researchers and practitioners in selecting effective techniques for cloud task scheduling systems.
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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.
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Citations
Dynamic Multi-Objective Service Resource Scheduling via LLM-Optimized Fuzzy State Fusion and Reinforcement Learning Closed Loop
Zhengzuo Li,Dianhui Chu,Zhiying Tu,Xin Hu,Deqiong Ding,Zhengzuo Li,Dianhui Chu,Zhiying Tu,Xin Hu,Deqiong Ding +9 more
An Improved DOA for Global Optimization and Cloud Task Scheduling
Abstract: Symmetry is an essential characteristic in both solution spaces and cloud task scheduling loads, as it reflects a structural balance that can be exploited to enhance algorithmic efficiency and robustness. In recent years, with the rapid development of 6G networks, the number of tasks requiring computation in the cloud has surged, prompting an increasing number of researchers to focus on how to efficiently schedule these tasks to idle computing nodes at low cost to enhance system resource utilization. However, developing reliable and cost-effective scheduling schemes for cloud computing tasks in real-world environments remains a significant challenge. This paper proposes a method for cloud computing task scheduling in real-world environments using an improved dhole optimization algorithm (IDOA). First, we enhance the quality of the initial population by employing a uniform distribution initialization method based on the Sobol sequence. Subsequently, we further improve the algorithm’s search capabilities using a sine elite population search method based on adaptive factors, enabling it to more effectively explore promising solution spaces. Additionally, we propose a random mirror perturbation boundary control method to better address individual boundary violations and enhance the algorithm’s robustness. By explicitly leveraging symmetry characteristics, the proposed algorithm maintains balanced exploration and exploitation, thereby improving convergence stability and scheduling fairness. To evaluate the effectiveness of the proposed algorithm, we compare it with nine other algorithms using the IEEE CEC2017 test set and assess the differences through statistical analysis. Experimental results demonstrate that the IDOA exhibits significant advantages. Finally, to verify its applicability in real-world scenarios, we applied IDOA to cloud computing task scheduling problems in actual environments, achieving excellent results and successfully completing cloud computing task scheduling planning.