Machine Learning in Energy and Thermal-aware Resource Management of Cloud Data Centers: A Taxonomy and Future Directions

Cloud data centres (CDCs) are the backbone infrastructures of modern digital society, but they also consume huge amounts of energy and generate heat. To manage CDC resources efficiently, we must consider the complex interactions between diverse workloads and data centre components. However, most existing resource management systems rely on simple and static rules that fail to capture these complex interactions. Therefore, we require new data-driven Machine learning-based resource management approaches that can efficiently capture the interdependencies between parameters and guide resource management systems. This review describes the in-depth analysis of the existing resource management approaches in CDCs for energy and thermal efficiency. It mainly focuses on learning-based resource management systems in data centres and also identifies the need for integrated computing and cooling systems management. A taxonomy on energy and thermal efficient resource management in data centres is proposed. Furthermore, based on this taxonomy, existing resource management approaches from server level, data centre level, and cooling system level are discussed. Finally, key future research directions for sustainable Cloud computing services are proposed.