Multi-Objective Optimization Techniques in Cloud Task Scheduling: A Systematic Literature Review
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 f...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10843235/ |
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| author | Olanrewaju L. Abraham Md Asri Bin Ngadi Johan Bin Mohamad Sharif Mohd Kufaisal Mohd Sidik |
| author_facet | Olanrewaju L. Abraham Md Asri Bin Ngadi Johan Bin Mohamad Sharif Mohd Kufaisal Mohd Sidik |
| author_sort | Olanrewaju L. Abraham |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-4da95dc518684573af6ba5e8888fe72e |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-4da95dc518684573af6ba5e8888fe72e2025-01-24T00:02:05ZengIEEEIEEE Access2169-35362025-01-0113122551229110.1109/ACCESS.2025.352983910843235Multi-Objective Optimization Techniques in Cloud Task Scheduling: A Systematic Literature ReviewOlanrewaju L. Abraham0https://orcid.org/0009-0009-8320-5784Md Asri Bin Ngadi1https://orcid.org/0000-0003-4907-6359Johan Bin Mohamad Sharif2Mohd Kufaisal Mohd Sidik3https://orcid.org/0009-0000-5518-2372Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, MalaysiaFaculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, MalaysiaFaculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, MalaysiaV3X Malaysia Sdn Bhd, Johor Bahru, Johor, MalaysiaTask 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.https://ieeexplore.ieee.org/document/10843235/Task schedulingmulti-objectiveoptimizationcloud computingmetaheuristic |
| spellingShingle | Olanrewaju L. Abraham Md Asri Bin Ngadi Johan Bin Mohamad Sharif Mohd Kufaisal Mohd Sidik Multi-Objective Optimization Techniques in Cloud Task Scheduling: A Systematic Literature Review IEEE Access Task scheduling multi-objective optimization cloud computing metaheuristic |
| title | Multi-Objective Optimization Techniques in Cloud Task Scheduling: A Systematic Literature Review |
| title_full | Multi-Objective Optimization Techniques in Cloud Task Scheduling: A Systematic Literature Review |
| title_fullStr | Multi-Objective Optimization Techniques in Cloud Task Scheduling: A Systematic Literature Review |
| title_full_unstemmed | Multi-Objective Optimization Techniques in Cloud Task Scheduling: A Systematic Literature Review |
| title_short | Multi-Objective Optimization Techniques in Cloud Task Scheduling: A Systematic Literature Review |
| title_sort | multi objective optimization techniques in cloud task scheduling a systematic literature review |
| topic | Task scheduling multi-objective optimization cloud computing metaheuristic |
| url | https://ieeexplore.ieee.org/document/10843235/ |
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