A Gradient Technique-Based Adaptive Multi-Agent Cloud-Based Hybrid Optimization Algorithm

被引：0
|
作者：
Ahmed, Mohammad Nadeem [1 ]
Hussain, Mohammad Rashid [2 ]
Husain, Mohammad [3 ]
Alshahrani, Abdulaziz M. [3 ]
Khan, Imran Mohd [4 ]
Ali, Arshad [3 ]
机构：
[1] Department of Computer Science-College of Computer Science, King Khalid University, Abha, Saudi Arabia
[2] Department of Business Informatics-College of Business, King Khalid University, Abha, Saudi Arabia
[3] Department of Computer Science-Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah, Saudi Arabia
[4] Department of Computer Engineering-College of Computer Science, King Khalid University, Abha, Saudi Arabia
来源：
International Journal of Advanced Computer Science and Applications | 2024年 / 15卷 / 11期
关键词：
Efficient virtual machine (VM) movement and task scheduling are crucial for optimal resource utilization and system performance in cloud computing. This paper introduces AMS-DDPG; a novel approach combining Deep Deterministic Policy Gradient (DDPG) with Adaptive Multi-Agent strategies to enhance resource allocation. To further refine AMS-DDPG’s performance; we propose ICWRS; which integrates WSO (Workload Sensitivity Optimization) and RSO (Resource Sensitivity Optimization) techniques for parameter fine-tuning. Experimental evaluations demonstrate that ICWRS-enabled AMS-DDPG significantly outperforms traditional methods; achieving a 25% improvement in resource utilization and a 30% reduction in task completion time; thereby enhancing overall system efficiency. By merging nature-inspired optimization techniques with deep reinforcement learning; our research offers innovative solutions to the challenges of cloud resource allocation. Future work will explore additional optimization methods to further advance cloud system performance. © (2024); (Science and Information Organization). All rights reserved;
D O I：
10.14569/IJACSA.2024.0151170
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页码：729 / 738
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