Approximate analysis of priority scheduling systems using stochastic reward nets

We present a performance analysis of a heterogeneous multiprocessor system where tasks may arrive from Poisson sources as well as by spawning and probabilistic branching of other tasks. Non-preemptive priority scheduling is used between different tasks. We use Stochastic Reward Nets as our system model, and solve it analytically by generating the underlying continuous-time Markov chain. We use an approximation technique based on fixed-point iteration to avoid the problem of a large underlying Markov chain. The iteration scheme works reasonably well, and the existence of a fixed point for our iterative scheme is guaranteed under certain conditions.

Duke Scholars

Author Kishor S. Trivedi Electrical and Computer Engineering