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Formal Reliability Models for Web Services

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Correct Software in Web Applications and Web Services

Part of the book series: Texts & Monographs in Symbolic Computation ((TEXTSMONOGR))

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Abstract

In Web services (WS), software applications are dynamically built by assembling over a network existing, loosely coupled, distributed, and heterogeneous services. Reliability is one of the most important quality dimensions for Web services, since predicting their reliability is fundamental to appropriately drive the selection and the assembly of services. This chapter presents two approaches to predict the reliability of a Web service architecture. The first one is based on the Business Process Execution Language (BPEL), the de facto standard executable language for specifying actions within business processes with Web services. The second one is based on the SCA-ASM, a lightweight formal language for modeling service-oriented applications, which is based on the OASIS (Organization for the Advancement of Structured Information Standards) standard Service Component Architecture for heterogeneous service assembly and on the formal method abstract state machines (ASMs) for modeling service behavior, interactions, and orchestration in an abstract but executable way. Through a set of experimental results, we show how the two models work on a smartphone mobile application example, and we discuss the effectiveness of the SCA-ASM approach in comparison with the BPEL-based approach.

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Notes

  1. 1.

    Such assumption is not too restrictive. It is a common practice in many reliability modeling approaches (see, e.g., the survey [28]).

  2. 2.

    BPEL defines business processes using an XML-based language. There is no standard graphical notation for BPEL. Some vendors have invented their own notations. Consider the standard Business Process Model and Notation (BPMN) [10] as a graphical front end to capture BPEL process descriptions.

  3. 3.

    Two grammatical conventions must be recalled: a variable identifier starts with $ and a rule identifier begins with “r_.”

  4. 4.

    The usage profile may be not (fully) available. In such cases, the domain knowledge and the information provided by the SOA could be used for estimating it, as suggested, for example, in [38].

  5. 5.

    For the sake of model linearity, as in [44], when writing expressions, we consider the logarithm of the reliability rather than the reliability itself.

  6. 6.

    We recall from [36] that an SCA-ASM component has a distinguished rule name of arity zero, taking by convention the same name as the component (e.g., rule r_A in Fig. 3 for component A). This rule is assigned as a program to the component’s agent created during the initialization, and it is used as entry point for the component execution.

  7. 7.

    More precisely, m yields the number of rule constructors occurring in the rules, services, and program of the SCA-ASM component providing the service k.

  8. 8.

    For the sake of model linearity, as in [44], we consider the logarithm of the reliability rather than the reliability itself.

  9. 9.

    Let us recall (see Definition 2.4.5 in [6]) that consistency of updates guarantees that an ASM location is never simultaneously updated to different values.

  10. 10.

    Recall that an invariant expresses a constraint one wants to assume for some functions of the ASM signature. Such constraints are stated as first-order formulas that have to hold in every state of the ASM.

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Authors and Affiliations

  1. Politecnico di Milano, Milano, Italy

    Raffaela Mirandola

  2. Università degli Studi di Bergamo, Dalmine, (BG), Italy

    Pasqualina Potena & Patrizia Scandurra

  3. Università degli Studi di Milano, Crema, Italy

    Elvinia Riccobene

Authors
  1. Raffaela Mirandola
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  2. Pasqualina Potena
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  3. Elvinia Riccobene
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  4. Patrizia Scandurra
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Correspondence to Patrizia Scandurra .

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Editors and Affiliations

  1. Institut für Informatik, Christian-Albrechts-Universität, Kiel, Germany

    Bernhard Thalheim

  2. Software Competence Center, Hagenberg, Austria

    Klaus-Dieter Schewe

  3. ICT Department, University of Agder, Kristiansand, Norway

    Andreas Prinz

  4. RISC, Johannes Kepler University, Hagenberg, Austria

    Bruno Buchberger

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Mirandola, R., Potena, P., Riccobene, E., Scandurra, P. (2015). Formal Reliability Models for Web Services. In: Thalheim, B., Schewe, KD., Prinz, A., Buchberger, B. (eds) Correct Software in Web Applications and Web Services. Texts & Monographs in Symbolic Computation. Springer, Cham. https://doi.org/10.1007/978-3-319-17112-8_7

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