Adaptive Reinforcement Learning for Dynamic Resource Allocation in Cloud Computing
Abstract
Efficient resource allocation is a critical challenge in cloud computing, where workloads are dynamic and unpredictable. This paper presents an adaptive reinforcement learning algorithm designed to optimize resource allocation in real-time, improving system efficiency and reducing operational costs. The proposed method leverages a reward-based mechanism to dynamically adjust resource distribution based on workload patterns, ensuring minimal latency and optimal utilization of computational resources. Extensive simulations on cloud environments demonstrate the algorithm's superiority over traditional static allocation methods, particularly in handling variable workloads. The results indicate that this adaptive approach not only enhances performance but also reduces energy consumption, making it a viable solution for sustainable cloud operations. The study concludes by discussing potential extensions to multi-cloud and hybrid cloud scenarios, paving the way for broader applicability.
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