A general and efficient framework for improving Balanced Failure Biasing

@article{Mao2020AGA,
  title={A general and efficient framework for improving Balanced Failure Biasing},
  author={Shi-Song Mao and M. Zhang and Jiaohong Yan and Yao Chen},
  journal={2020 IEEE 20th International Conference on Software Quality, Reliability and Security Companion (QRS-C)},
  year={2020},
  pages={445-450}
}
  • S. MaoM. Zhang Yao Chen
  • Published 1 December 2020
  • Computer Science
  • 2020 IEEE 20th International Conference on Software Quality, Reliability and Security Companion (QRS-C)
Balanced Failure Biasing (BFB) is a way to simulate the probability of reaching a rare goal state in highly reliable Markovian systems (HRMSs). BFB gives the same probability to each ralely-arrived path of one state, therefore leading to large expenditures on paths with little influence on results. We propose a new framework using Stratified Sampling, which is a general and efficient framework for improving BFB. We introduce Stratified Sampling on BFB (SBFB), which divides the original state… 

Figures and Tables from this paper

References

SHOWING 1-10 OF 17 REFERENCES

Path-ZVA: general, efficient and automated importance sampling for highly reliable Markovian systems

Graph analysis is used to obtain knowledge of the shortest paths (in terms of `rare' transitions) to the goal state of a Markov chain, and it is shown that only a subset of the state space needs to be considered.

Failure Transition Distance-Based Importance Sampling Schemes for theSimulation of Repairable Fault-Tolerant Computer Systems

  • J. Carrasco
  • Engineering
    IEEE Transactions on Reliability
  • 2006
Two importance sampling schemes are developed, which exploit the failure transition distance concept in an attempt to improve the efficiency of two other schemes, failure biasing & and balanced failureBiasing, which are used to evaluate dependability attributes of fault-tolerant computer systems.

Measure specific dynamic importance sampling for availability simulations

Analysis of a simple three state Birth and Death process shows that additional variance reduction over that previously reported can be obtained by simulating the numerator and denominator independently with different dynamic importance sampling distributions.

Failure distance-based simulation of repairable fault-tolerant systems

This paper presents a new importance sampling scheme called failure biasing for the efficient simulation of Markovian models of repairable fault-tolerant systems. The new scheme enriches the failure

A Unified Framework for Simulating Markovian Models of Highly Dependable Systems

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

Varaince reduction in mean time to failure simulations

We describe two variance reduction methods for estimating the mean time to failure (MTTF) in Markovian models of highly reliable systems. The first method is based on a ratio representation of the

Simulation and analysis of highly reliable systems

This thesis investigates simulation algorithms and numerical approximations for estimating performance measures for some large classes of highly reliable systems and develops a mathematical framework within which it can prove that the modified failure biasing technique yields a rate of convergence that is insensitive to the component failure rates.

Fast Simulation of Markov Chains with Small Transition Probabilities

Two importance-sampling techniques are developed that have the bounded relative error property, i.e., the simulation run-length required to estimate the rare-event probability to a fixed degree of accuracy remains bounded as the event of interest becomes more rare.

Monte Carlo simulation of Markov unreliability models

Monte Carlo Simulation of Computer System Availability / Reliability Models

This paper investigates the use of Monte Carlo simulation as an alternative for solving models with a large number of components and shows that the Importance Sampling variance reduction technique may be applied to reduce the simulation run lengths substantially.