VeriFog: A Generic Model-based Approach for Verifying Fog Systems at Design Time and Generating Deployment Configurations - Laboratoire des Sciences du Numérique de Nantes
Journal Articles ACM SIGAPP applied computing review : a publication of the Special Interest Group on Applied Computing Year : 2024

VeriFog: A Generic Model-based Approach for Verifying Fog Systems at Design Time and Generating Deployment Configurations

Abstract

Fog Computing is a paradigm decentralizing the Cloud by geographically distributing computation, storage, network resources and related services. It provides benefits such as reducing the number of bottlenecks, limiting unwanted data movements, etc. However, managing the size, complexity and heterogeneity of the Fog systems to be engineered is challenging and can quickly become costly. According to best practices in software engineering, verification tasks could be performed on a system design prior to its implementation and deployment. We propose a generic model-based approach for verifying Fog systems at design time, also enabling the automatic generation of corresponding deployment configuration files. Named VeriFog, this approach is notably based on a customizable Fog Modeling Language (FML). We experimented in practice by modeling three use cases, from three different application domains, and by considering three main types of non-functional properties to be verified. From this modeling and verification effort, we show that we are able to automatically generate usable deployment configuration files for different deployment tools. In direct collaboration with our industrial partner Smile, the approach and underlying language presented in this paper are necessary steps towards a more global model-based support for the complete life cycle of Fog systems.
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hal-04727206 , version 1 (09-10-2024)

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Hiba Awad, Abdelghani Alidra, Hugo Bruneliere, Thomas Ledoux, Jonathan Rivalan. VeriFog: A Generic Model-based Approach for Verifying Fog Systems at Design Time and Generating Deployment Configurations. ACM SIGAPP applied computing review : a publication of the Special Interest Group on Applied Computing, 2024, 24 (3), pp.18 - 36. ⟨10.1145/3699839.3699841⟩. ⟨hal-04727206⟩
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