Leveraging Multi-Armed Bandits for Cloud Resource Optimization

Cloud computing has become a critical infrastructure in modern IT services, offering scalable and flexible resources to a wide range of applications. Efficient resource allocation in such a dynamic and unpredictable environment remains a significant challenge. This paper introduces a novel approach to cloud resource allocation by leveraging game theory and the Multi-Armed Bandit (MAB) theorem. By modeling resource allocation as a game between competing tasks, we employ MAB strategies to optimize decision-making under uncertainty, balancing exploration and exploitation. The proposed method enhances resource utilization, reduces latency, and ensures fairness among tasks. Key findings demonstrate that our approach outperforms traditional allocation methods in various scenarios, leading to more efficient and equitable resource distribution. Thus, by integrating game theory and MAB strategies, this research provides a robust framework for dynamic resource allocation in cloud computing environments. The results highlight the potential for improved performance and fairness, paving the way for more effective cloud resource management.