Assessing the Impact of Imperfect Diagnosis on Service Reliability: A Parsimonious Model Approach
Modelling imperfect diagnosis performance in service reliability models can help identify best recovery strategies and diagnosis settings. In this work, a parsimonious Markov model of imperfect diagnosis is proposed. Capturing complex diagnosis behavior in the model is non-trivial. In our approach, representative diagnosis performance metrics have been defined and their closed-form solutions obtained for the Markov model. These equations enable model parameterization from traces of implemented diagnosis components. The diagnosis model has been integrated in a reliability model assessing the impact of diagnosis imperfections on reliability for time-constrained SCTP/TCP-based services. This enables: (a) a model-based sensitivity analysis of the service reliability to the diagnosis performance metrics, and (b) investigation of whether the chosen metrics provide a sufficiently detailed characterization of the diagnosis functions for the studied reliability problem. In a simulation study we finally analyze trade-off properties of diagnosis heuristics from literature, map them to the analytic Markov model, and investigate its suitability for service reliability optimization.