Intelligent Resource Allocation Optimization for Cloud Computing via Machine Learning

Yuqing Wang

Abstract:

With the rapid expansion of cloud computing applications, optimizing resource
allocation has become crucial for improving system performance and cost efficiency. This
paper proposes an intelligent resource allocation algorithm that leverages deep learning
(LSTM) for demand prediction and reinforcement learning (DQN) for dynamic scheduling.
By accurately forecasting computing resource demands and enabling real-time adjustments,
the proposed system enhances resource utilization by 32.5%, reduces average response
time by 43.3%, and lowers operational costs by 26.6%. Experimental results in a
production cloud environment confirm that the method significantly improves efficiency
while maintaining high service quality. This study provides a scalable and effective
solution for intelligent cloud resource management, offering valuable insights for future
cloud optimization strategies.