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Approximate analysis of priority scheduling systems using stochastic reward nets

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2 Author(s)
Mainkar, V. ; Dept. of Comput. Sci., Duke Univ., Durham, NC, USA ; Trivedi, K.S.

Presents 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. Stochastic reward nets are used as the system model, and are solved analytically by generating the underlying continuous-time Markov chain. An approximation technique is used, that is 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 the iterative scheme is guaranteed under certain conditions

Published in:

Distributed Computing Systems, 1993., Proceedings the 13th International Conference on

Date of Conference:

25-28 May 1993