A software tool for learning about stochastic models

Author

Sahner, Robin A. ; Trivedi, Kishor S.

Author_Institution

Motorola, Urbana, IL, USA

Volume

36

Issue

1

fYear

1993

fDate

2/1/1993 12:00:00 AM

Firstpage

56

Lastpage

61

Abstract

The Symbolic Hierarchical Automated Reliability/Performance Evaluator (SHARPE), a software system that analyzes stochastic models, is discussed. SHARPE allows students to set up and solve a variety of model types, to compare results for different models of the same system, to see how altering system parameters affects measures of effectiveness of the system, and to experiment with modeling techniques, including the use of exact and approximate system or model decomposition. It can also be used to illustrate problems of large state spaces and stiff systems and to provide examples of methods for avoiding these problems. Using SHARPE, one can specify and analyze the following model types separately or in combination: fault trees, reliability block diagrams, reliability graphs, product-form queuing networks, series-parallel acyclic directed graphs, Markov and semi-Markov chains, and generalized stochastic Petri nets

Keywords

Markov processes; Petri nets; computer aided instruction; education; failure analysis; probability; reliability theory; software packages; teaching; CAI; Markov chains; SHARPE; Symbolic Hierarchical Automated Reliability/Performance Evaluator; block diagrams; decomposition; education; failure analysis; fault trees; generalized stochastic Petri nets; graphs; learning; modeling; probability; product-form queuing networks; reliability theory; semi-Markov chains; series-parallel acyclic directed graphs; software tool; state spaces; stiff systems; stochastic models; students; Algorithm design and analysis; Computer network reliability; Computer science; Education; Fault trees; Mathematics; Software algorithms; Software tools; Stochastic processes; Stochastic systems;

fLanguage

English

Journal_Title

Education, IEEE Transactions on

Publisher

ieee

ISSN

0018-9359

Type

jour

DOI

10.1109/13.204817

Filename

204817