A major problem in performance evaluation of real-life industrial systems is the enormous number of states generated by the system's model during the simulation process. An example is in a Parallel Object-Oriented Specification Language (POOSL) model, which can be interpreted as a discrete-time Markov chain enabling the evaluation of long-run average performance metrics. In general this Markov chains is huge, making performance simulation expensive since all performance estimations must be updated in each state. In this paper, we introduce a method for performance evaluation based on Markov chain theory, which aims at the reduction of the original Markov chain into a smaller chain. We prove that the reduction yields another Markov chain and it preserves several important properties. We further give a method to effectively construct the reduced chain. The reduction method yields an efficient way to estimate performance metrics during model simulation, since in general it requires estimations to be updated only in a relatively small part of the state space.
|Title of host publication||Proceedings 2nd PROGRESS Workshop on Embedded Systems (Utrecht, The Netherlands, October 18, 2001)|
|Publisher||STW Technology Foundation|
|Publication status||Published - 2001|