A clustering-based knowledge discovery process for data centre infrastructure management

Abstract

Data centre infrastructure management (DCIM) is the integration of information technology and facility management disciplines to centralise monitoring and management in data centres. One of the most important problems of DCIM tools is the analysis of the huge amount of data obtained from the real-time monitoring of thousands of resources. In this paper, an adaptation of the knowledge discovery process for dealing with the data analysis in DCIM tools is proposed. A case of study based on monitoring and labelling of nodes of a high performance computing data centre in real time is presented. This shows that characterising the state of the nodes according to a reduced and relevant set of metrics is feasible and its outcome directly usable, simplifying consequently the decision-making process in these complex infrastructures. © 2016, Springer Science+Business Media New York.