Distributed State-Space Generation of Discrete-State Stochastic Models.
Abstract
High-level formalisms such as stochastic Petri nets can be used to model complex systems. Analysis of logical and numerical properties of these models of ten requires the generation and storage of the entire underlying state space. This imposes practical limitations on the types of systems which can be modeled. Because of the vast amount of memory consumed we investigate distributed algorithms for the generation of state space graphs. The distributed construction allows us to take advantage of the combined memory readily available on a network of workstations. The key technical problem is to find effective methods for on-the-fly partitioning so that the state space is evenly distributed among processors. In this paper we report on the implementation of a distributed state-space generator that may be linked to a number of existing system modeling tools. We discuss partitioning strategies in the context of Petri net models and report on performance observed on a network of workstations as well as on a distributed memory multi-computer. (AN)
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 01, 1995
- Accession Number
- ADA302432
Entities
People
- David Nicol
- Gianfranco Ciardo
- Joshua Gluckman