Performance Assessment of Time Series Forecasting Models for Cloud Datacenter Networks’ Workload Prediction

Abstract

The resource scaling has been influential in enabling the cloud service providers to provision the resources on-demand effectively. The prior estimation of workloads helps in addressing the scaling issues arises due to dynamic nature of the resource demands. In this paper, we evaluate six different forecasting approaches over real world workload data traces of web and cloud servers. The entire analysis is carried out three times as three different functions are used to measure the deviation in forecasts. The three forecast error measures are root mean squared error, mean absolute error, and mean absolute scaled error. We also carried out a statistical evaluation using Friedman test and Finner post-hoc analysis. The study concludes that the auto ARIMA process outperforms other models and achieves the best rank in the statistical analysis.