Exploratory data analysis for data center energy management

The continuous improvement in energy efficiency of existing data centers would help reduce their environmental footprints. Greening of Data Centers could be attained using renewable energy sources or more energy efficient compute systems and effective cooling systems. A reliable cooling system is necessary to generate a persistent flow of cold air to cool servers that are subjected to increasing computational load demand. As a matter of fact, servers' dissipated heat effects a strain on the cooling systems and consequently, on electricity consumption. 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. The cooling and environmental aspects of the data center is also considered for data analysis. 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.