In: Microelectronics and Reliability, Vol. 35, No. 6, pages 903-914. 1995.
Abstract: Dependability modeling plays a major role in the design, validation and maintenance of real-time computing systems. Typical models provide measures such as mean time to failure, reliability and safety as functions of the component failure rates and fault/error coverage probabilities. This paper proposes a modeling technique that allows the coverage to be dependent upon the local (i.e. embedded at task level) and global (i.e. available at system level) fault/error detection and recovery mechanisms. This approach also ensures important savings in terms of the simulation time required for deriving the coverage probabilities. Stochastic reward nets are employed as a unique dependability modeling framework. For illustrating the usefulness of this technique, we analyze dependability of a railroad control computer.
Keywords: dependability modeling, fault detection, fault recovery, real-time systems, stochastic Petri nets.
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