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A feature-based survey of Fog modeling languages

Authors :
Abdelghani Alidra
Hugo Bruneliere
Thomas Ledoux
Département Automatique, Productique et Informatique (IMT Atlantique - DAPI)
IMT Atlantique (IMT Atlantique)
Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)
Software Stack for Massively Geo-Distributed Infrastructures (STACK)
Inria Rennes – Bretagne Atlantique
Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire des Sciences du Numérique de Nantes (LS2N)
Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique)
Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-École Centrale de Nantes (Nantes Univ - ECN)
Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes université - UFR des Sciences et des Techniques (Nantes univ - UFR ST)
Nantes Université - pôle Sciences et technologie
Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes Université - pôle Sciences et technologie
Nantes Université (Nantes Univ)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique)
Nantes Université (Nantes Univ)
NaoMod - Nantes Software Modeling Group (LS2N - équipe NaoMod)
Laboratoire des Sciences du Numérique de Nantes (LS2N)
Nantes Université (Nantes Univ)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique)
ANR-20-CE25-0017,SeMaFoR,Gestion autonome des ressources dans le Fog computing(2020)
Source :
Future Generation Computer Systems, Future Generation Computer Systems, 2023, 138, pp.104-119. ⟨10.1016/j.future.2022.08.010⟩
Publication Year :
2023
Publisher :
Elsevier BV, 2023.

Abstract

International audience; Fog Computing is a new paradigm aiming at decentralizing the Cloud by geographically distributing away computation, storage and network resources as well as related services. In order to design, develop, deploy, maintain and evolve Fog systems, languages are required for properly modeling both their entities (e.g., infrastructures, topologies, resources configurations) and their specific features such as the locality concept, QoS constraints applied on resources (e.g., energy, data privacy, latency) and their dependencies, the dynamicity of considered workloads, the heterogeneity of both applications and devices, etc. This paper provides a detailed overview of the current state-of-the-art in terms of Fog modeling languages. We relied on our long-term experience in Cloud Computing and Cloud Modeling to contribute a feature model describing what we believe to be the most important characteristics of Fog modeling languages. We also performed a systematic scientific literature search and selection process to obtain a list of already existing Fog modeling languages. Then, we evaluated and compared these Fog modeling languages according to the characteristics expressed in our feature model. As a result, we discuss in this paper the main capabilities of these Fog modeling languages and propose a corresponding set of open research challenges in this area. We expect the presented work to be helpful to both current and future researchers or engineers working on/with Fog systems, as well as to anybody genuinely interested in Fog Computing or more generally in distributed systems.

Details

ISSN :
0167739X
Volume :
138
Database :
OpenAIRE
Journal :
Future Generation Computer Systems
Accession number :
edsair.doi.dedup.....cb5cf9f18284bff336f7a84164d7ac02