PRACTISE: Robust prediction of data center time series

Abstract

We analyze workload traces from production data centers and focus on their VM usage patterns of CPU, memory, disk, and network bandwidth. Burstiness is a clear characteristic of many of these time series: there exist peak loads within clear periodic patterns but also within patterns that do not have clear periodicity. We present PRACTISE, a neural network based framework that can efficiently and accurately predict future loads, peak loads, and their timing. Extensive experimentation using traces from IBM data centers illustrates PRACTISE's superiority when compared to ARIMA and baseline neural network models, with average prediction errors that are significantly smaller. Its robustness is also illustrated with respect to the prediction window that can be short-term (i.e., hours) or long-term (i.e., a week).