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"Quantitative Verification for Monitoring Event-Streaming Systems" by Guoxin Su, Li Liu et al.

High-performance data streaming technologies are increasingly adopted in IT companies to support the integration of heterogeneous and possibly distributed applications. Compared with the traditional message queuing middleware, a streaming platform enables the implementation of event-streaming systems (ESS) which include not only complex queues but also pipelines that transform and react to the streams of data. By analysing the centralised data streams, one can evaluate the Quality-of-Service for other systems and components that produce or consume those streams. We consider the exploitation of probabilistic model checking as a performance monitoring technique for ESS systems. Probabilistic model checking is a mature, powerful verification technique with successful application in performance analysis. However, an ESS system may contain quantitative parameters that are determined by event streams observed in a certain period of time. In this paper, we present a novel theoretical framewor ....

Discrete Time Markov Chain , Event Stream , Arametric Model Checking , Performance Monitoring , Robabilistic Model Checking , Statistical Inference ,