Grid performance prediction using state-space model
One of the main challenges of scheduling algorithms in Grid environment is the autonomy of sites, which makes it difficult for the grid scheduler to estimate the exact cost of a task execution on different sites. In this paper, we present a solution for this problem based on data history (workload traces) and time series techniques. The main focus of this work is devoted to forecasting the task waiting time in a resource queue, which is under the control of a local scheduler with distinctive scheduling policy. The main contribution of this work is the consideration of a special property of the grid resources, the dynamic membership, i.e. a resource may exit and then come back to the grid environment repeatedly. When the resource belongs to the grid environment, its workload trace (log file) is considered as a correct log. On the other hand, when the resource leaves the grid, the log file during this period is considered as a defective part of the trace. As the defective parts contain some useful information, after repairing these defective parts, they can be used for forecasting purposes. Of this, we employ state-space model along with the associated Kalman recursions in conjunction with the Expectation-Maximization algorithm to repair the defective waiting time series such as a correct log file by which the resource seems never to have left the grid. The experimental results on a number of workload logs demonstrated that this approach can achieve an average prediction error, between 22 and 64% less than those incurred by other rival methods. Copyright (C) 2009 John Wiley & Sons, Ltd.
performance prediction state-space model scheduling kalman recursions grid computing