A unified framework for simulating Markovian models of highly dependable systems

Author

Goyal, Ambuj ; Shahabuddin, Penvez ; Heidelberger, Philip ; Nicola, Victor F. ; Glynn, Peter W.

Author_Institution

IBM Thomas J. Watson Res. Center, Yorktown Heights, NY, USA

Volume

41

Issue

1

fYear

1992

fDate

1/1/1992 12:00:00 AM

Firstpage

36

Lastpage

51

Abstract

The authors present a unified framework for simulating Markovian models of highly dependable systems. It is shown that a variance reduction technique called importance sampling can be used to speed up the simulation by many orders of magnitude over standard simulation. This technique can be combined very effectively with regenerative simulation to estimate measures such as steady-state availability and mean time to failure. Moveover, it can be combined with conditional Monte Carlo methods to quickly estimate transient measures such as reliability, expected interval availability, and the distribution of interval availability. The authors show the effectiveness of these methods by using them to simulate large dependability models. They discuss how these methods can be implemented in a software package to compute both transient and steady-state measures simultaneously from the same sample run

Keywords

Markov processes; Monte Carlo methods; digital simulation; Monte Carlo methods; highly dependable systems; importance sampling; mean time to failure; regenerative simulation; reliability; simulating Markovian models; software package; steady-state availability; steady-state measures; transient measures; unified framework; variance reduction technique; Availability; Computational modeling; Context modeling; Degradation; Discrete event simulation; Measurement standards; Monte Carlo methods; State estimation; Steady-state; Time measurement;

fLanguage

English

Journal_Title

Computers, IEEE Transactions on

Publisher

ieee

ISSN

0018-9340

Type

jour

DOI

10.1109/12.123381

Filename

123381