Fast Simulation of Dependability Models with General Failure, Repair and Maintenance Processes
Abstract
The problem of computing dependability measures of repairable systems with general failure, repair and maintenance processes is a hard problem to solve in general either by analytical or by numerical methods. Monte Carlo simulation could be used to solve this problem, however, standard simulation takes a very long time to estimate system reliability and availability with reasonable accuracy because typically the system failure is a rare event. When the failure and repair time distributions are exponential, importance sampling has been used successfully in the past to reduce simulation run lengths. In this paper, we extend the applicability of importance sampling to non-Markovian models with general failure and repair time distributions. We show that by carefully selecting a heuristic for importance sampling, orders of magnitude reduction in simulation run-lengths can be obtained. We illustrate the effectiveness of the technique by modeling a large repairable computing system. Also, we study the effect of periodic maintenance on systems with components having increasing and decreasing failure rate.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 1990
- Accession Number
- ADA228970
Entities
People
- Ambuj Goyal
- Marvin K. Nakayama
- Philip Heidelberger
- Victor F. Nicola
Organizations
- Stanford University