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Capturing Stochastic and Real-Time Behavior in Reo Connectors

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Formal Methods: Foundations and Applications (SBMF 2017)

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Abstract

Modern distributed systems are often coupled with flexible architectures, composed of heterogenous components, and deployed on different execution nodes. Under such frameworks, connectors (or middlewares) are widely used to organize the separated components and make them functioning. Apparently, reliability of such systems highly depends on the correctness of their connectors. Reo is a channel-based coordination language where complex connectors are constructed from simpler ones through a compositional approach. In this paper, we propose a stochastic and real-time extension of Reo, including a set of new primitive channels and an expressive semantics named Stochastic Timed Automata for Reo (\(\text {STA}_r\)). With the support of \(\text {STA}_r\), different coordination scenarios in existing Reo extensions can be easily encoded, integrated, and analyzed.

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Notes

  1. 1.

    Different interpretations of AsyncDrain have been proposed in [2, 6]. For simplicity we choose the later one in [6], and don’t consider fairness issues.

  2. 2.

    Proof can be found at https://github.com/liyi-david/ReoSTA.

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Acknowledgements

The work was partially supported by the National Natural Science Foundation of China under grant no. 61532019, 61202069 and 61272160.

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Authors and Affiliations

  1. Department of Informatics and LMAM, School of Mathematical Sciences, Peking University, Beijing, China

    Yi Li, Xiyue Zhang, Yuanyi Ji & Meng Sun

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  1. Yi Li
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  2. Xiyue Zhang
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  3. Yuanyi Ji
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  4. Meng Sun
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Correspondence to Meng Sun .

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Editors and Affiliations

  1. Universidade Federal de Pelotas, Pelotas, Brazil

    Simone Cavalheiro

  2. Royal Holloway, University of London, Egham, United Kingdom

    José Fiadeiro

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Li, Y., Zhang, X., Ji, Y., Sun, M. (2017). Capturing Stochastic and Real-Time Behavior in Reo Connectors. In: Cavalheiro, S., Fiadeiro, J. (eds) Formal Methods: Foundations and Applications. SBMF 2017. Lecture Notes in Computer Science(), vol 10623. Springer, Cham. https://doi.org/10.1007/978-3-319-70848-5_18

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  • DOI: https://doi.org/10.1007/978-3-319-70848-5_18

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