Cluster Computing and the Grid, IEEE International Symposium on (2006)
May 16, 2006 to May 19, 2006
The ability to accurately predict future resource capabilities is of great importance for applications and scheduling algorithms which need to determine how to use time-shared resources in a dynamic grid environment. In this paper we present and evaluate a new and innovative method to predict the one-stepahead CPU load in a grid. Our prediction strategy forecasts the future CPU load based on the tendency in several past steps and in previous similar patterns, and uses a polynomial fitting method. Our experimental results demonstrate that this new prediction strategy achieves average prediction errors that are between 37% and 86% lower than those incurred by the previously best tendency-based method.
Grid computing, Information science, Polynomials, Processor scheduling, Time series analysis, Sun, Scheduling algorithm, Load forecasting, Internet, Aggregates
"CPU Load Predictions on the Computational Grid *," Cluster Computing and the Grid, IEEE International Symposium on(CCGRID), Singapore, 2013, pp. 321-326.