Optimizing Data Centre Energy Efficiency with Dynamic Resource Allocation and Intelligent Cooling Management through Machine Learning

Abstract

Our research aims to improve energy efficiency in data centres by combining cloud computing infrastructure with machine learning techniques. We propose that dynamic resource assignment, combined with intelligent optimisation of cooling systems, can reduce power waste and operational costs. Real-time sensor data from various data centre components such as servers, cooling systems, and power distribution units is collected and fed into machine learning models for analysis. In this way, we can create power arrangements that are tailored to the resources and needs of various applications. The experimental results show that energy consumption has been reduced by an average of 30% compared to traditional methods. Furthermore, our machine learning models are quite accurate in predicting cooling system performance. For example, an Artificial Neural Network (ANN) has an accuracy rate of 98.78%. This result demonstrates the efficacy of our approach in promoting energy efficiency and operational performance in data centres: it not only provides a scalable, cost-effective solution to industry energy efficiency challenges, but it also improves day-to-day data centre operations by reducing electrical consumption. Our approach, which is based on dynamic allocation of computational resources and real-time data analysis for optimising cooling systems, not only saves energy but also improves operational efficiency. With energy in mind, we must all work towards a more sustainable and green approach to data centre management. Future research can look into other potential optimisations, as well as issues with scalability and application in real-world data centre environments.