A Forecasting Methodology for Workload Forecasting in Cloud Systems

Abstract—Cloud Computing is an essential paradigm of computing services based on the “elasticity” property, where available resources are adapted efficiently to different workloads over time. In elastic platforms, the forecasting component can be considered by far the most important element and the differentiating factor when comparing such systems, being workload forecasting one of the problems to solve if we want to achieve a truly elastic system. When properly addressed the cloud workload forecasting problem becomes a really interesting case study. As there is no general methodology in the literature that addresses this problem analytically and from a time series forecasting perspective (even less in the Cloud field), we propose a combination of these tools based on a state-of-theart forecasting methodology which we have enhanced with some elements, such as: a specific cost function, statistical tests, visual analysis, etc. The insights obtained from this analysis are used to find the best forecasting from the viewpoint of the current state of the art in time series forecasting. To show the feasibility of this methodology, we apply it to several realistic workload dataset from different datacenters. The results show that the analyzed series are non-linear in nature, and that no seasonal patterns can be found. Moreover,on the analyzed datasets, the penalty cost as usually included in the SLA can be reduced down to a 30% on average.

Index Terms—Cloud Computing, elasticity, workload forecasting, machine learning, time series forecasting.