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Towards a Model Level Replication Technique for Fault Tolerant Systems Using AADL

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Part of the Studies in Computational Intelligence book series (SCI,volume 612)

Abstract

The replication, a technique widely used for fault tolerance purposes, is defined as the redundancy of software, hardware or both units and their consideration in the execution of the application. In this paper, we propose a new technique to design replication using the AADL language and its extensibility with property sets. We choose AADL to take advantage of its strong semantics at architecture level. We enable the designer to model his application using AADL and to enrich it with the property set Replication_Properties. We defined this property set to describe the adopted concepts of replication. Then, based on a set of transformation rules, we generate an intermediate AADL model enriched with different replicas. Currently, we are extending the Ocarina tool suite to support automatic generation of the target model.

Keywords

  • Model Transformation
  • Fault Tolerance
  • Transformation Rule
  • Active Replication
  • Consensus Algorithm

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Fig. 1
Fig. 2
Fig. 3

Notes

  1. 1.

    Recovery Block.

  2. 2.

    N Self-Checking Programming.

  3. 3.

    N-Version Programming.

  4. 4.

    More details about the Replication_Properties property set, the transformation algorithms and the case study are available at http://goo.gl/EEQhLK.

  5. 5.

    The textual model generated after applying the list of transformation rules of the model shown in Fig. 3 and enriched with properties in listing 2 is available at http://goo.gl/EEQhLK.

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Correspondence to Wafa Gabsi .

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Gabsi, W., Zalila, B. (2016). Towards a Model Level Replication Technique for Fault Tolerant Systems Using AADL. In: Lee, R. (eds) Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing 2015. Studies in Computational Intelligence, vol 612. Springer, Cham. https://doi.org/10.1007/978-3-319-23509-7_12

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  • DOI: https://doi.org/10.1007/978-3-319-23509-7_12

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