Reliability Of Run-Time Quality-Of-Service Evaluation Using Parametric Model Checking

Run-time Quality-of-Service (QoS) assurance is crucial for business-critical systems. Complex behavioral performance metrics (PMs) are useful but often difficult to monitor or measure. Probabilistic model checking, especially parametric model checking, can support the computation of aggregate functions for a broad range of those PMs. In practice, those PMs may be defined with parameters determined by run-time data. In this paper, we address the reliability of QoS evaluation using parametric model checking. Due to the imprecision with the instantiation of parameters, an evaluation outcome may mislead the judgment about requirement violations. Based on a general assumption of run-time data distribution, we present a novel framework that contains light-weight statistical inference methods to analyze the reliability of a parametric model checking output with respect to an intuitive criterion. We also present case studies in which we test the stability and accuracy of our inference methods and describe an application of our framework to a cloud server management problem.