The Framework for Performance Modeling and Evaluation of Parallel Job Scheduling Algorithms
The performance of job scheduling algorithms in campus-wide PC-cluster distributed computing environment may be influenced by several input variables (factors) such as sum of the job sizes of all the jobs in the workload, number of PCs in the cluster and even on the type of scheduling algorithm being used. Response Surface Methodology (RSM) based statistical regression techniques build empirical model for performance prediction of the scheduling system by means of mathematical equation that relate the scheduler performance (response) to the input process parameters. Artificial Neural Networks (ANNs) can also be successfully employed for modeling of complex non-linear prediction problems. Feed-forward ANN models viz.