Deep Learning for Cloud Cluster Management: Classifying and Optimizing Cloud Clusters to Improve Data Center Scalability and Efficiency

Abstract

The proliferation of cloud computing has led to an exponential increase in the scale and complexity of cloud data centers, necessitating more sophisticated approaches for managing and monitoring cloud clusters. Traditional rule-based systems are often inadequate to cope with the dynamic nature of cloud environments, where workloads fluctuate rapidly and resource allocation must be optimized in real-time. This research explores the integration of deep learning techniques into cloud cluster management, with a specific focus on classifying clusters based on their behavioral patterns and optimizing resource usage. Deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory networks (LSTMs), and autoencoders, offer powerful tools for analyzing time-series data generated by cloud clusters. These models can detect latent patterns, predict future resource demands, and automate decision-making processes, leading to improved scalability and efficiency in cloud data centers. The paper also addresses the challenges associated with deploying deep learning in cloud environments, such as the need for extensive training data, the risk of model overfitting, and the computational overhead involved in real-time monitoring and inference. This research aims to provide a framework for applying deep learning to automate the classification, management, and monitoring of cloud clusters to increase the operational efficiency of modern cloud infrastructures.