Machine Learning-Based Workload Prediction for Energy Optimization in Data Centers

This research presents a machine learning-based framework for predicting workload patterns in data centers to enable energy-efficient operations. The study proposes a hybrid model combining Long Short-Term Memory (LSTM) networks and Random Forest algorithms to improve prediction accuracy and support dynamic resource allocation. The model is evaluated using real-world datasets, including large-scale cluster traces and institutional data center logs. Experimental results show that the hybrid approach outperforms individual models, achieving higher prediction accuracy and enabling energy savings of up to 25% through adaptive power scaling. The paper also discusses implementation challenges such as concept drift, system integration, and scalability, while outlining future directions including attention mechanisms, federated learning, and reinforcement learning for intelligent energy management. This work contributes to sustainable computing by demonstrating how predictive analytics can significantly reduce energy consumption in modern data centers without compromising performance.