Statistical Modeling and Evaluation of Parallel Space-sharing Job Scheduling Algorithms for PC-cluster using Design of Experiments (DOE)

Parallel space-sharing job scheduling algorithms play an indispensible role in efficient allocation of processors of PC-cluster to the competing jobs to achieve one of the performance objective(s) viz. minimized mean response time (MRT), minimized average bounded slowdown or maximized throughput. Traditional performance modeling and evaluation studies of parallel spacesharing job scheduling algorithms are incompetent of predicting the combined or interaction effect on the response resulting due to simultaneous variation of two process variables. Present work is undertaken to predict and quantize the influence of main and interaction effects of the input scheduling process variables on the output MRT values using statistical approach of design of experiments (DOE). DOE based Response surface methodology (RSM) oriented experimental design is chosen to evaluate MRT values for two scheduling algorithms namely First Come First Serve (FCFS) and Fit Processors First Served (FPFS). Two empirical interaction models are suggested for both scheduling algorithms that predict MRT on the basis of multiple regression equations involving main and interaction effect terms of scheduling process variables. High value of adjusted coefficient of determination R and insignificant lack of fit represent the goodness of fit of both the models to accurately predict the MRT values. Both the empirical interaction models are validated against additional experimental results. The comparative performance evaluation study on the basis of MRT reveals that the FPFS algorithm tends to outweigh the traditional FCFS policy.