Fast Anomaly Detection in Micro Data Centers Using Machine Learning Techniques

Abstract

This paper proposes a new approach to fast detection of abnormal behaviour of cooling and IT systems in micro data centers (MDCs) based on machine learning (ML) techniques. Conventional protection of MDCs focuses on monitoring individual parameters such as temperature at different locations and when these parameters reaches certain high values, then alarm will be triggered. This paper employs ML techniques to extract normal and abnormal behaviour of the cooling and IT systems. Developed data acquisition system together with unsupervised learning methods quickly learns the physical dynamics of normal operation and is able to detect deviations from such behaviours. This provides an efficient way for not only producing health index for the MDC, but also a rich label logging system that will be used for the supervised learning methods. The effectiveness of the proposed detection technique is evaluated on a MDC placed at Computing Infrastructure Research Center (CIRC) in McMaster Innovation Park (MIP), McMaster University.