Time-series models for cloud workload prediction: A comparison

Dynamic cloud workloads necessitate forecasting methodologies for accurate resource provisioning affecting both cloud providers and clients. This paper focuses on forecasting in the cloud in order to understand its underlying workload dynamics. It analyzes recent workload traces and discovers characteristics that are not adequately captured by traditional linear & nonlinear models employed for forecasting in the cloud. This paper completes a comprehensive statistical analysis of 8 workloads realized from production cloud environments. Through characterization, time-series elicitation and model fitting, it isolates a limited but important set of statistical distributions that capture cloud traffic dynamics. Furthermore, it adopts a recent econometric modeling technique called the Autoregressive Conditional Score (ACS) model that improves forecasting accuracy over existing methods. To exploit our findings from the workload characterization of the traces, we also extend the ACS model to realize a variant called ACS-l that models errors using the lognormal distribution. Compared with existing models, the ACS-l offers a 10%-25% improvement in forecasting accuracy when right-tailed distributions are observed in workloads. Furthermore, the score-based characteristics observed in time-series and their diversity has inspired a novel classification of cloud workloads into three distinct groups according to the most appropriate model: linear, nonlinear and hybrid models. A methodology that employs statistical measures to guide this selection has also been developed.