Exploratory data analysis for data center energy management

Generated heat in the data center is categorized into different granularity levels namely: server level, rack level, room level, and data center level. Several datasets are collected at ENEA Portici Data Center from CRESCO 6 cluster - a High-Performance Computing Cluster. This research aims to conduct a rigorous exploratory data analysis on each dataset separately and collectively followed in various stages. This work presents descriptive and inferential analyses for feature selection and extraction process. Furthermore, a supervised Machine learning modelling and correlation estimation is performed on all the datasets to abstract relevant features, that would have an impact on energy efficiency in data centers.