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

Abstract

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.