Modeling and evaluation of task scheduling algorithms using Stochastic Petri Net

The multiprocessor scheduling problem is defined as finding an optimal schedule which minimizes the parallel execution time of an parallel program. Since if has been shown that this problem is NP-complete, research efforts have resulted in numerous scheduling algorithms based on heuristics. The need for measuring the effectiveness of the various task scheduling algorithms leads to a great demand on modeling and evaluation fools. The paper proposes to use the Stochastic Petri Net model and its equivalent Continuous Time Markov Chain as the tool for modeling and evaluation of task scheduling algorithms. The proposed approach is applied to DFRN scheduling algorithm to investigate its effectiveness. The proposed approach is verified by comparing the result obtained from the SPN model with that obtained by actually running DFRN algorithm. The performance comparison reveals that the proposed approach provides very accurate performance evaluation for the scheduling algorithm when CCR value is small.