Quantitative Verification for Monitoring Event-Streaming Systems

Abstract

IEEE High-performance data streaming technologies are increasingly adopted in IT companies to support the integration of heterogeneous and possibly distributed service systems and 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 applications 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 the 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 framework called QV4M (meaning "quantitative verification for monitoring") for ESS system monitoring based on two recent methods of probabilistic model checking. Our framework QV4M assumes the parameters in a probabilistic system model as random variables and infers the statistical confidence of a probabilistic model checking output. We present two case studies as an empirical evaluation of QV4M.

A review of microservices autoscaling with formal verification perspective

“…In short, it will only provide quantitative results as the outcomes besides the Boolean result (yes or no) for each property checked by the model checker. For instance, research done by Ray and Banerjee 61 is utilizing DTMC to model the failure scenarios of multiaccess edge computing (MEC) servers as a part of the Stochastic Multiplayer Games (SMG) strategy while the research performed by Su et al 62 is modelling the microservice orchestration system with parametric DTMC.…”

“…Meanwhile, the specification that still has not been used widely for microservice verification are probabilistic alternating‐time temporal logic with rewards (rPATL), linear temporal logic (LTL), labeled transition system (LTS), probabilistic reward computation tree logic (PRCTL), ontology rules and performance evaluation process algebra (PEPA). Furthermore, the properties are used to verify several aspects of the microservices which are the interaction soundness, 86 realizability of microservice choreography, 88 failure points and resource usage, 7 performance monitoring, 62 deadlock and livelock freedom, 87,89 resiliency patterns 60,84 and security characteristics 85 . Finally, unlike the other works, the study performed by Ray and Banerjee 61 synthesized fault‐recovery strategies in MEC.…”

“…The next elements observed in verification for microservices is the model specification in which dominated by CTMC 7,60,84 that has been explained in Section 4.5.2 and followed by DTMC, 61,62 MDP, 61,64 CSP# Process 86,88 and TB Petri Nets. 87,89 Meanwhile, the combination of Ontology Web Language (OWL) and Architecture Description Language (ADL) 85 is the least model used for the verification purpose.…”