Predictive Algorithms for Resource Utilization and Server Overload Management in Dynamic Cloud Environments

Abstract

Cloud computing has revolutionized modern computing with scalable, flexible, and on-demand resources, but efficient resource management remains a critical challenge due to issues like server overloads, energy consumption, and unpredictable demands. This paper introduces the Ensemble Energy Prediction Resource Utilization Algorithm (EEPRUA), a novel solution designed to manage resource utilization and prevent server overload in dynamic cloud environments. The proposed system incorporates machine learning techniques, including Linear Regression (LR), Exponential Moving Average (EMA), and Long Short-Term Memory (LSTM), to predict resource usage patterns in real-time. EPRUA dynamically allocates cloud resources, preventing both underutilization and overloading. The algorithm was rigorously tested in a simulated cloud environment, demonstrating significant improvements in energy efficiency, resource utilization, and overall system stability. By reducing operational costs and optimizing performance, EPRUA provides cloud providers with a powerful tool to ensure high-quality service even under fluctuating workloads, while also promoting energy-efficient and sustainable cloud operations.