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Towards a formal reference computational model for cloud configuration management

Philippe Merle 1 Souha Ben Rayana 2 Lionel Seinturier 1 Roger Pissard-Gibollet 2 Jean-Bernard Stefani 2 Adja Ndeye Sylla 3
1 SPIRALS - Self-adaptation for distributed services and large software systems
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
2 SPADES [2020-....] - Sound Programming of Adaptive Dependable Embedded Systems [2020-....]
LIG [2020-....] - Laboratoire d'Informatique de Grenoble [2020-....], Inria Grenoble - Rhône-Alpes
Abstract : The multiplication of models, languages, APIs and tools for cloud and network configuration management raises heterogeneity issues that can be tackled by introducing a reference model. A reference model provides a common basis for interpretation for various models and languages, and for bridging different APIs and tools. This report formally specifies, in the Alloy specification language, a reference model for cloud configuration management, we call the Cloudnet Computational Model. We show how to formally interpret several configuration languages in it, including the TOSCA configuration language, the OpenStack Heat Orchestration Template, the Docker Compose configuration language, and the Aeolus cloud deployment model. We show in particular how the formal operational semantics of our Cloudnet computation model allows us to extend the TOSCA standard with Aeolus concepts for deployment lifecycle, and how the Alloy formalization allowed us to discover several classes of errors in the OpenStack HOT specification.
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https://hal.inria.fr/hal-02940938
Contributor : Jean-Bernard Stefani <>
Submitted on : Wednesday, September 16, 2020 - 4:41:07 PM
Last modification on : Tuesday, September 29, 2020 - 12:24:14 PM

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  • HAL Id : hal-02940938, version 1

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Philippe Merle, Souha Ben Rayana, Lionel Seinturier, Roger Pissard-Gibollet, Jean-Bernard Stefani, et al.. Towards a formal reference computational model for cloud configuration management. [Research Report] RR-9317, INRIA. 2020. ⟨hal-02940938⟩

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