Department of CSE, Government Polytechnic, Kudligi, Karnataka, India.
Review Article
World Journal of Advanced Research and Reviews, 2020, 08(02), 377-385
Received on 11 November 2020; revised on 20 November 2020; accepted on 21 November 2020
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.
Deep Reinforcement Learning; Reinforcement learning techniques; IoT-edge-cloud computing; resource allocation; resource management
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Channappa A. Intelligent resource allocation in multi-cloud environments using deep reinforcement learning. World Journal of Advanced Research and Reviews, 2020, 8(2), 377-385. Article DOI: https://doi.org/10.30574/wjarr.2020.8.2.0397