Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic inference and influence diagrams
Operations Research
Software metrics (2nd ed.): a rigorous and practical approach
Software metrics (2nd ed.): a rigorous and practical approach
Learning Bayesian networks with local structure
Learning in graphical models
An analysis of factors affecting software reliability
Journal of Systems and Software
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Software Reliability
Software Reliability Engineered Testing
Software Reliability Engineered Testing
Journal of Computer Security - IFIP 2000
Software Measurement and Estimation: A Practical Approach (Quantitative Software Engineering Series)
Software Measurement and Estimation: A Practical Approach (Quantitative Software Engineering Series)
Learning Bayesian Networks
Modeling Business Process Availability
SERVICES '08 Proceedings of the 2008 IEEE Congress on Services - Part I
Analytical availability assessment of IT services
ISAS'08 Proceedings of the 5th international conference on Service availability
Parameterising bayesian networks
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
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Ensuring the availability of enterprise IT systems is a challenging task. The factors that can bring systems down are numerous, and their impact on various system architectures is difficult to predict. At the same time, maintaining high availability is crucial in many applications, ranging from control systems in the electric power grid, over electronic trading systems on the stock market to specialized command and control systems for military and civilian purposes. This paper describes a Bayesian decision support model, designed to help enterprise IT system decision-makers evaluate the consequences of their decisions by analyzing various scenarios. The model is based on expert elicitation from 50 experts on IT systems availability, obtained through an electronic survey. The Bayesian model uses a leaky Noisy-OR method to weigh together the expert opinions on 16 factors affecting systems availability. Using this model, the effect of changes to a system can be estimated beforehand, providing decision support for improvement of enterprise IT systems availability. The Bayesian model thus obtained is then integrated within a standard, reliability block diagram-style, mathematical model for assessing availability on the architecture level. In this model, the IT systems play the role of building blocks. The overall assessment framework thus addresses measures to ensure high availability both on the level of individual systems and on the level of the entire enterprise architecture. Examples are presented to illustrate how the framework can be used by practitioners aiming to ensure high availability.