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

2007 ◽  
Author(s):  
N. Sato ◽  
K. S. Trivedi
Keyword(s):  
Reliability Analysis ◽  
Composite Services ◽  
Markov Reward ◽  
Stochastic Reliability ◽  
Markov Reward Model

A Symbolic Approach to the Analysis of Multi-Formalism Markov Reward Models

2013 ◽  
pp. 170-195
Author(s):  
Kai Lampka ◽  
Markus Siegle
Keyword(s):  
Functional System ◽  
State Variables ◽  
State Charts ◽  
Level Model ◽  
System Properties ◽  
Markov Reward ◽  
High Level ◽  
Symbolic Approach ◽  
State Space Generation ◽  
Markov Reward Model

When modelling large systems, modularity is an important concept, as it aids modellers to master the complexity of their model. Moreover, employing different modelling formalisms within the same modelling project has the potential to ease the description of various parts or aspects of the overall system. In the area of performability modelling, formalisms such as stochastic reward nets, stochastic process algebras, stochastic automata, or stochastic UML state charts are often used, and several of these may be employed within one modelling project. This chapter presents an approach for efficiently constructing a symbolic representation in the form of a zero-suppressed Binary Decision Diagram (BDD), which represents the Markov Reward Model underlying a multi-formalism high-level model. In this approach, the interaction between the submodels may be established either by the sharing of state variables or by the synchronisation of common activities. It is shown that the Decision Diagram data structure and the associated algorithms enable highly efficient state space generation and different forms of analysis of the underlying Markov Reward Model (e.g. calculation of reward measures or asserting non-functional system properties by means of model checking techniques).

Machine learning aided stochastic reliability analysis of spatially variable slopes

2020 ◽  
Vol 126 ◽  
pp. 103711
Author(s):  
Xuzhen He ◽  
Haoding Xu ◽  
Hassan Sabetamal ◽  
Daichao Sheng
Keyword(s):  
Machine Learning ◽  
Reliability Analysis ◽  
Stochastic Reliability
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