Accurate and efficient stochastic reliability analysis of composite services using their compact Markov reward model representations

Stochastic reliability analysis of composite services is challenging,
primarily since it needs us to carefully balance accuracy of analysis
and its computational complexity:
Given stochastic models of service components, we often combine them
and define a large complex model to accurately reflect the effects of
failures of particular components on the reliability of the entire
service.
In this paper, we propose a new technique, based on the Markov reward
model (MRM) foundation, to substantially reduce the computational
complexity without losing accuracy.
It evaluates, prior to analysis, the effects of the possible failures
and represents them as scalar reward values attached to a single
compact Markov model.
Thus we can replace the component models with a compact model
that retains the complete information for accurate analysis.
We demonstrate the effectiveness of this technique for several cases,
where failures are correlated with each other in different ways.