CNN and Deep Q-Learning-Enhanced Cloud Networking: Integrating SDN with Neural Networks for Intelligent Resource Management

Abstract

The proposed intelligent cloud resource management framework integrates Software-Defined Networking 
(SDN), Convolutional Neural Networks (CNN), and Deep Q-Learning to optimize resource allocation in 
cloud computing environments. SDN dynamically manages network resources, ensuring real-time 
adaptability to fluctuating demands, while CNN is used for feature extraction from cloud performance 
metrics such as CPU usage, memory usage, and network traffic. This provides actionable insights for more 
efficient resource allocation. The Deep Q-Learning component further enhances decision-making by 
continuously adjusting resource management strategies based on feedback from the cloud environment. The 
framework's effectiveness is validated using the Cloud Computing Performance Metrics Dataset, 
demonstrating significant improvements in key performance areas. Key metrics include 78% CPU utilization, 
reduced task completion time to 150 ms, and energy efficiency boosted to 92%. Compared to traditional 
models like LSTM and SVM, the proposed framework outperforms in both resource utilization and system 
efficiency. This combination of SDN, CNN, and Deep Q-Learning enables the framework to dynamically 
optimize cloud resource allocation, addressing the challenges of scalability and efficient resource 
management in real-world cloud environments.