AI-Based Virtual Machine Resource Optimization in Cloud Computing

Abstract

Virtual machines are a fundamental component of modern cloud computing infrastructures, allowing multiple applications to operate on shared physical resources. Efficient virtual machine resource management is essential for maintaining application performance while minimizing resource wastage. Traditional virtual machine management techniques often use static configurations or predefined resource thresholds, which may not respond effectively to changing workloads. Artificial Intelligence (AI) provides intelligent methods for monitoring virtual machine behavior, predicting resource requirements, and dynamically optimizing resource allocation. This paper presents an AI-based approach for virtual machine resource optimization in cloud computing environments. The proposed framework monitors CPU, memory, storage, and network utilization and uses machine learning techniques to predict resource requirements. Based on these predictions, resources can be dynamically adjusted to improve utilization and maintain application performance. An experimental evaluation can be performed using simulated virtual machine workloads with different resource requirements. The expected results indicate that AI-based optimization can reduce resource wastage, improve virtual machine utilization, and support efficient cloud infrastructure management.