Incremental Change Detection Method For Data Center Power Efficiency Metrics (Work In Progress Paper)

Abstract

We propose an incremental change detection method for data center (DC) energy efficiency metrics and consider its application to the power usage efficiency (PUE) metric. In recent years, there is an increasing focus on the sustainability of DCs and PUE is playing an important role to evaluate the DC's energy efficiency. Publicly reported PUE values are mostly calculated over a whole year as there are many fluctuations caused by outside influences as outdoor air temperature (OAT). In this paper, we propose a method to detect short-term changes in the DC energy efficiency (e.g., PUE), while considering outside influences (e.g., OAT) observing related daily aggregated DC data. We also conduct a few preliminary experiments for PUE change detection based on real-world DC data, where we have manually labeled changes in the PUE using visualization tools. The experimental results show that the method can detect important major and minor changes in the PUE with a very low false positive rate. However, due to the small number of positive labels, the recall rate is currently between 57% and 70%. Further investigation is necessary to see how representative the current recall rates are and what kind of improvements are necessary to make the change detection method more stable.