In: IEEE Computer Soc. Press, Proc. of 6th International Workshop on Petri Nets and Performance Models - PNPM'95, Durham, N. Carolina, USA, pages 52-61. 1995.
Also in: IEEE Transactions on Software Engineering, Volume 22, Issue 9, pages 615-628. IEEE Press, September 1996.
Abstract: The numerical analysis of various modeling formalisms profits from a structured representation for the generator matrix Q of the underlying continuous time Markov chain, where Q is described by a sum of tensor (knonecker) products of much smaller matrices. In this paper we describe such a representation for the class of superposed generalized stochastic Petri nets (SGSPNs), which is less restrictive than descriptions given before. Furthermore a new iterative analysis algorithm is proposed which pays special attention to a memory efficient representation of iteration vectors as well as to a memory efficient, structured representation of Q. In consequence the new algorithm is able to solve models which have state spaces with several millions of states and where other exact numerical methods become impracticable on a common workstation.
Keywords: Stochastic Petri net; superposed GSPN; Markov process; numerical solution algorithm for steady-state analysis tensor/Kronecker algebra; decomposition; reachability analysis; structured representation..