sensitivity analysis of reliability and performability measures for multiprocessor systems
Traditional evaluation techniques for multiprocessor systems use Markov chains and Markov reward models to compute measures such as mean time to failure, reliability, performance, and performability. In this paper, we discuss the extension of Markov models to include parametric sensitivity analysis. Using such analysis, we can guide system optimization, identify parts of a system model sensitive to error, and find system reliability and performability bottlenecks. As an example we consider three models of a 16 processor. 16 memory system. A network provides communication between the processors and the memories. Two crossbar-network models and the Omega network are considered. For these models, we examine the sensitivity of the mean time to failure, unreliability, and performability to changes in component failure rates. We use the sensitivities to identify bottlenecks in the three system models.
sensitive sensitivity analysis analysis reliability performance measurement multiprocessor processor systems evaluation use model computation mean timing failure paper extensibility parametric optimality system modeling errors examples memory system memorialized network communication change component network model