Seasonal study of user demand and IT system usage in datacenters

To limit the impact of datacenters on climate change various sustainable and effective solutions are being developed. The Datazero2 project aims to design datacenters running solely on local renewable energy combined with storage sources. A multi-hour prevision of user demand (workload) or of IT system usage, using time series, are possible ways to plan energy usage, with the aim of reducing energy consumption while maintaining a required quality of service. The seasonal component of time series is a key element, since it indicates one or more periodic phenomena that can be estimated and implemented in forecasting methods. In this paper, we propose a seasonal study of multiple time series from different workloads. Seasonality is studied using periodograms, a highly effective Fast Fourier Transform technique for detecting seasonal patterns. In addition, since the detected seasonal patterns can be irregular (unexpected, delayed and/or extended user demands or IT system usage), we propose a distribution-based clustering using the Kruskal-Wallis test and a seasonal peak appearances analysis. The results show that most of these time series are multi-seasonal (daily and weekly seasonal patterns) but highly irregular, which can reduce the performance of seasonal forecasting methods.