Q-Automata: Modelling the Resource Usage of Concurrent Components

Chothia, Tom; Kleijn, Jetty

doi:10.1016/j.entcs.2007.03.009

Cited by 25 publications

(37 citation statements)

References 15 publications

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“…Given such a model, a Q-algebra can be used to compute the total delay of a synchronous transaction, cf. [21]. Thus, assuming that the time delays to transfer a request to the Authenticate service through the channel Sync(H, I) and to the Authorize service though the channel Sync(H, J) equal t 1 and t 2 , respectively, the total time delay for the transition H(x)|I(x)|J(x) can be computed as max(t 1 , t 2 ).…”

Section: Case Studymentioning

confidence: 99%

Reo + mCRL2 : A framework for model-checking dataflow in service compositions

2012

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Abstract. The paradigm of service-oriented computing revolutionized the field of software engineering. According to this paradigm, new systems are composed of existing stand-alone services to support complex cross-organizational business processes. Correct communication of these services is not possible without a proper coordination mechanism. The Reo coordination language is a channel-based modeling language that introduces various types of channels and their composition rules. By composing Reo channels, one can specify Reo connectors that realize arbitrary complex behavioral protocols. Several formalisms have been introduced to give semantics to Reo. In their most basic form, they reflect service synchronization and dataflow constraints imposed by connectors. To ensure that the composed system behaves as intended, we need a wide range of automated verification tools to assist service composition designers. In this paper, we present our framework for the verification of Reo using the mCRL2 toolset. We unify our previous work on mapping various semantic models for Reo, namely, constraint automata, timed constraint automata, coloring semantics and the newly developed action constraint automata, to the process algebraic specification language of mCRL2, address the correctness of this mapping, discuss tool support, and present a detailed example that illustrates the use of Reo empowered with mCRL2 for the analysis of dataflow in service-based process models.

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Section: Case Studymentioning

confidence: 99%

Reo + mCRL2 : A framework for model-checking dataflow in service compositions

2012

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“…Operations over these parameters can be formally specified using a notion of Q-algebra [15]. A Q-algebra is an algebraic structure R = (C, ⊕, ⊗, ||, 0, 1) where C is the domain of R and represents a set of QoS values.…”

Section: Is a Finite Set Of Clocks And Ic : S → CC Is A Function Tmentioning

confidence: 99%

Formal Behavioral Modeling and Compliance Analysis for Service-Oriented Systems

Kokash

Arbab

2009

Formal Methods for Components and Objects

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Abstract. In this paper, we present a framework for formal modeling and verification of service-based business processes with focus on their compliance to external regulations such as Segregation of Duties (SoD) or privacy protection policies. In our framework, control/data flow is modeled using the exogenous coordination language Reo. Reo process models are designed from scratch or (semi-)automatically obtained from BPMN, UML or WS-BPEL specifications. Constraint automata (CA), a semantic model for Reo, provide state-based representations of process workflows and enable their verification by means of model checking technology. Various extensions of CA make it possible to analyze time-, resource-and Quality-of-Service (QoS) process models.

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“…Quantitative Reo and QCA are extensions of Reo and CA, respectively, with quantitative aspects by Q-algebra [13] and form the basis for compositional specification and reasoning on QoS issues for connectors. A Q-algebra is an algebraic structure R = (C, ⊕, ⊗, , 0, 1) such that R ⊗ = (C, ⊕, ⊗, 0, 1) and R = (C, ⊕, , 0, 1) are both constraint semirings [9,20].…”

Section: Quantitative Constraint Automata and Quantitative Reomentioning

confidence: 99%

From Coordination to Stochastic Models of QoS

Arbab

Chothia

Mei

et al. 2009

Lecture Notes in Computer Science

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Science. It is sponsored by the Netherlands Organisation for Scientific Research (NWO).CWI is a founding member of ERCIM, the European Research Consortium for Informatics and Mathematics.CWI's research has a theme-oriented structure and is grouped into four clusters. Listed below are the names of the clusters and in parentheses their acronyms. From coordination to stochastic models of QoS ABSTRACT Reo is a channel-based coordination model whose operational semantics is given by Constraint Automata (CA). Quantitative Constraint Automata extend CA (and hence, Reo) with quantitative models to capture such non-functional aspects of a system's behaviour as delays, costs, resource needs and consumption, that depend on the internal details of the system. However, the performance of a system can crucially depend not only on its internal details, but also on how it is used in an environment, as determined for instance by the frequencies and distributions of the arrivals of I/O requests. In this paper we propose Quantitative Intentional Automata (QIA), an extension of CA that allow incorporating the influence of a system's environment on its performance. Moreover, we show the translation of QIA into ContinuousTime Markov Chains (CTMCs), which allows us to apply existing CTMC tools and techniques for performance analysis of QIA and Reo circuits. Abstract. Reo is a channel-based coordination model whose operational semantics is given by Constraint Automata (CA). Quantitative Constraint Automata extend CA (and hence, Reo) with quantitative models to capture such non-functional aspects of a system's behaviour as delays, costs, resource needs and consumption, that depend on the internal details of the system. However, the performance of a system can crucially depend not only on its internal details, but also on how it is used in an environment, as determined for instance by the frequencies and distributions of the arrivals of I/O requests. In this paper we propose Quantitative Intentional Automata (QIA), an extension of CA that allow incorporating the influence of a system's environment on its performance. Moreover, we show the translation of QIA into Continuous-Time Markov Chains (CTMCs), which allows us to apply existing CTMC tools and techniques for performance analysis of QIA and Reo circuits. Probability, Networks and Algorithms (PNA)SoftwareKeywords: Performance evaluation, Coordination language, Reo, MarkovChains. IntroductionService-oriented Computing (SOC) provides the means to design and deploy distributed applications that span organization boundaries and computing platforms by exploiting and composing existing services available over a network. Services are platform-and network-independent applications that support rapid, low-cost, loosely-coupled composition. Services run on the hardware of their own providers, in different containers, separated by fire-walls and other ownership and trust barriers. Their composition requires additional mechanisms (e.g., process work-flow engines, connectors, or glue code) to impos...

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Q-Automata: Modelling the Resource Usage of Concurrent Components

Cited by 25 publications

References 15 publications

Reo + mCRL2 : A framework for model-checking dataflow in service compositions

Reo + mCRL2 : A framework for model-checking dataflow in service compositions

Formal Behavioral Modeling and Compliance Analysis for Service-Oriented Systems

From Coordination to Stochastic Models of QoS

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