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FARE: A Framework for Benchmarking Reliability of Cyber-Physical Systems

Wu, Leon; Kaiser, Gail E.

A cyber-physical system (CPS) is a system featuring a tight combination of, and coordination between, the system's computational and physical elements. System reliability is a critical requirement of cyber-physical systems. An unreliable CPS often leads to system malfunctions, service disruptions, financial losses and even human life. Improving CPS reliability requires an objective measurement, estimation and comparison of the CPS system reliability. This paper describes FARE (Failure Analysis and Reliability Estimation), a framework for benchmarking reliability of cyber-physical systems. Some prior researches have proposed reliability benchmark for some specific CPS such as wind power plant and wireless sensor networks. There were also some prior researches on the components of CPS such as software and some specific hardware. But according to the best of our knowledge, there isn't any reliability benchmark framework for CPS in general. FARE framework provides a CPS reliability model, a set of methods and metrics on the evaluation environment selection, failure analysis and reliability estimation for benchmarking CPS reliability. It not only provides a retrospect evaluation and estimation of the CPS system reliability using the past data, but also provides a mechanism for continuous monitoring and evaluation of CPS reliability for runtime enhancement. The framework is extensible for accommodating new reliability measurement techniques and metrics. It is also generic and applicable to a wide range of CPS applications. For empirical study, we applied the FARE framework on a smart building management system for a large commercial building in New York City. Our experiments showed that FARE is easy to implement, accurate for comparison and can be used for building useful industry benchmarks and standards after accumulating enough data.



More About This Work

Academic Units
Computer Science
Department of Computer Science, Columbia University
Columbia University Computer Science Technical Reports, CUCS-009-13
Published Here
April 5, 2013