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A Self-adaptive Agent-based System for Cloud Platforms

Soltane, Merzoug; Cardinale, Yudith; Angarita Arocha, Rafaël; Rosse, Philippe; Rukoz, Marta; Makhlouf, Derdour; Okba, Kazar (2018), A Self-adaptive Agent-based System for Cloud Platforms, in Amroune, Mohammed, 3rd International Conference on Pattern Analysis and Intelligent Systems (PAIS 2018), IEEE - Institute of Electrical and Electronics Engineers : Piscataway, NJ, p. 1-8. 10.1109/PAIS.2018.8598507

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1810.07622.pdf (1.180Mb)
Type
Communication / Conférence
Date
2018
Conference title
3rd International Conference on Pattern Analysis and Intelligent Systems (PAIS 2018)
Conference date
2018-10
Conference city
Tebessa
Conference country
Algeria
Book title
3rd International Conference on Pattern Analysis and Intelligent Systems (PAIS 2018)
Book author
Amroune, Mohammed
Publisher
IEEE - Institute of Electrical and Electronics Engineers
Published in
Piscataway, NJ
Pages
1-8
Publication identifier
10.1109/PAIS.2018.8598507
Metadata
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Author(s)
Soltane, Merzoug
Laboratoire d'Informatique Intelligente [LINFI]
Cardinale, Yudith cc
Universidad Simon Bolivar [USB]
Angarita Arocha, Rafaël
Institut Supérieur d'Electronique de Paris [ISEP]
Rosse, Philippe
Laboratoire Informatique de l'Université de Pau et des Pays de l'Adour [LIUPPA]
Rukoz, Marta
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Makhlouf, Derdour

Okba, Kazar
autre
Abstract (EN)
Cloud computing is a model for enabling on-demand network access to a shared pool of computing resources, that can be dynamically allocated and released with minimal effort. However, this task can be complex in highly dynamic environments with various resources to allocate for an increasing number of different users requirements. In this work, we propose a Cloud architecture based on a multi-agent system exhibiting a self-adaptive behavior to address the dynamic resource allocation. This self-adaptive system follows a MAPE-K approach to reason and act, according to QoS, Cloud service information, and propagated run-time information, to detect QoS degradation and make better resource allocation decisions. We validate our proposed Cloud architecture by simulation. Results show that it can properly allocate resources to reduce energy consumption, while satisfying the users demanded QoS.
Subjects / Keywords
Distributed; Parallel; Cluster Computing

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