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

中图分类号：

学科分类号：

摘要：

引用

页码：729 / 738

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