CoMe4ACloud: An End-to-End Framework for Autonomic Cloud Systems

Autor: Charles Prud'homme, Zakarea Alshara, Frederico Alvares, Jonathan Lejeune, Hugo Bruneliere, Thomas Ledoux
Přispěvatelé: Software Stack for Massively Geo-Distributed Infrastructures (LS2N - équipe 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), Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Département Automatique, Productique et Informatique (IMT Atlantique - DAPI), IMT Atlantique (IMT Atlantique), EASYVIRT, NaoMod - Nantes Software Modeling Group (LS2N - équipe NaoMod), Laboratoire des Sciences du Numérique de Nantes (LS2N), DistributEd aLgorithms and sYStems (DELYS), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-LIP6, Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Théorie, Algorithmes et Systèmes en Contraintes (LS2N - équipe TASC ), CoMe4ACloud (Atlanstic 2020), Software Stack for Massively Geo-Distributed Infrastructures (STACK), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS), NaoMod - Nantes Software Modeling Group (NaoMod), Théorie, Algorithmes et Systèmes en Contraintes (TASC )
Jazyk: angličtina
Rok vydání: 2018
Předmět:
Computer Networks and Communications
Computer science
Distributed computing
Cloud computing
02 engineering and technology
[INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE]
[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]
Autonomic computing
Constraint Programming
End-to-end principle
Autonomic Computing
0202 electrical engineering
electronic engineering
information engineering
Constraint programming
Layer (object-oriented design)
computer.programming_language
business.industry
020206 networking & telecommunications
Cloud Computing
Model Driven Engineering
Hardware and Architecture
020201 artificial intelligence & image processing
Model-driven architecture
[INFO.INFO-DC]Computer Science [cs]/Distributed
Parallel
and Cluster Computing [cs.DC]
business
computer
Software
Zdroj: Future Generation Computer Systems
Future Generation Computer Systems, 2018, 86, pp.339-354. ⟨10.1016/j.future.2018.03.039⟩
Future Generation Computer Systems, Elsevier, 2018, 86, pp.339-354. ⟨10.1016/j.future.2018.03.039⟩
ISSN: 0167-739X
Popis: International audience; Autonomic Computing has largely contributed to the development of self-manageable Cloud services. It notably allows freeing Cloud administrators of the burden of manually managing varying-demand services, while still enforcing Service-Level Agreements (SLAs). All Cloud artifacts, regardless of the layer carrying them, share many common characteristics. Thus, it should be possible to specify, (re)configure and monitor any XaaS (Anything-as-a-Service) layer in an homogeneous way. To this end, the CoMe4ACloud approach proposes a generic model-based architecture for autonomic management of Cloud systems. We derive a generic unique Autono-mic Manager (AM) capable of managing any Cloud service, regardless of the layer. This AM is based on a constraint solver which aims at finding the optimal configuration for the modeled XaaS, i.e. the best balance between costs and revenues while meeting the constraints established by the SLA. We evaluate our approach in two different ways. Firstly, we analyze qualitatively the impact of the AM behaviour on the system configuration when a given series of events occurs. We show that the AM takes decisions in less than 10 seconds for several hundred nodes simulating vir-tual/physical machines. Secondly, we demonstrate the feasibility of the integration with real Cloud systems, such as Openstack, while still remaining generic. Finally, we discuss our approach according to the current state-of-the-art.
Databáze: OpenAIRE
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