Intelligent resource allocation in multi-cloud environments using deep reinforcement learning

Abstract

The proliferation of multi-cloud environments has introduced complexities in resource management, necessitating intelligent solutions for optimal resource allocation. Traditional resource allocation techniques often struggle to adapt to the dynamic nature of multi-cloud ecosystems, leading to suboptimal performance, increased costs, and inefficient resource utilization. This paper explores the application of Deep Reinforcement Learning (DRL) as an advanced AI-driven approach to optimize resource distribution across multiple cloud platforms. By leveraging reinforcement learning techniques, DRL enables autonomous decision-making, learning from past experiences to refine resource allocation strategies in real-time. The proposed framework is designed to handle diverse workloads, minimize latency, and maximize cost-efficiency. Additionally, the study evaluates key performance metrics, including throughput, response time, and adaptability, comparing DRL-based approaches with traditional methods. Experimental results indicate that DRL significantly improves efficiency, scalability, and adaptability in dynamic cloud environments, paving the way for intelligent and automated cloud resource management.