Reinforcement Learning for Dynamic Resource Allocation in Cloud Computing
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Abstract
Efficient resource allocation is critical for optimizing cloud computing performance and cost. This paper explores the use of reinforcement learning (RL) algorithms to dynamically allocate computing resources in multi-tenant cloud environments. We propose a model that leverages deep RL to predict workload patterns and adjust resource provisioning in real-time. Experiments on simulated and real-world datasets demonstrate improved efficiency, reduced latency, and cost savings compared to traditional methods. Challenges such as scalability, convergence, and integration with existing systems are addressed, paving the way for intelligent cloud management.
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