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

TitleAccurate and efficient stochastic reliability analysis of composite services using their compact Markov reward model representations
Publication TypeJournal Article
Year of Publication2007
AuthorsN Sato, and KS Trivedi
JournalProceedings 2007 Ieee International Conference on Services Computing, Scc 2007
Start Page114
Pagination114 - 121
Date Published10/2007
Abstract

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. © 2007 IEEE.

DOI10.1109/SCC.2007.21
Short TitleProceedings 2007 Ieee International Conference on Services Computing, Scc 2007