Hourly server workload forecasting up to 168 hours ahead using Seasonal ARIMA model

Abstract

Data center workload prediction is important to take decisions in resources management system. Seasonal ARIMA model provide a good server workload methodology for the server workload forecasting. A large set of our experiments confirm that it has high performance, scalability and reliability and will bee integrated in our system. This paper presents a general expression in development of our forecast model in the project EnergeTic-FUI, France.