Predicting the Performance of a GRID Environment: An Initial Effort to Increase Scheduling Efficiency

Abstract

GRID environments are privileged targets for computation-intensive problem solving in areas from weather forecasting to seismic analysis. Mainly composed by commodity hardware, these environments can deliver vast computational capacity, at relatively low cost. In order to take full advantage of their power we need to have efficient task schedulers with the ability to maximize resource effectiveness, shortening execution times. GRID schedulers must not only decide taking a snapshot of the GRID's status into account, but should also consider the output involved in past decisions. In this work, we intend to show how resource usage can be analyzed, through the use of data mining techniques, to predict performance availability of a GRID environment, as a preliminary work to increase scheduling efficiency as well as adequate resource provisioning.