SAAM: A self-adaptive aggregation mechanism for autonomous management systems

Rafik Makhloufi 1 Guillaume Doyen 1 Grégory Bonnet 2 Dominique Gaïti 1
1 ERA - Environnement de Réseaux Autonomes
ICD - Institut Charles Delaunay
2 Equipe MAD - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen
Abstract : In this paper, we propose a decentralized Self-Adaptive Aggregation Mechanism (SAAM) that adapts itself to the supporting network operational behavior by dynamically selecting the best aggregation approach to use. SAAM is based on (1) a fuzzy-based model that estimates the cost and performance of each aggregation scheme and (2) Multiple Attribute Decision Making (MADM) to make decisions on the best approach to use in this context. We validate SAAM by evaluating its fuzzy model and adaptation cost, and by comparing its utility to the one of existing situated and global schemes.
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Conference papers
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https://hal-utt.archives-ouvertes.fr/hal-02274695
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Submitted on : Friday, August 30, 2019 - 10:34:14 AM
Last modification on : Monday, September 16, 2019 - 4:35:42 PM

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  • HAL Id : hal-02274695, version 1

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Rafik Makhloufi, Guillaume Doyen, Grégory Bonnet, Dominique Gaïti. SAAM: A self-adaptive aggregation mechanism for autonomous management systems. 2012 IEEE/IFIP Network Operations and Management Symposium (NOMS 2012), Apr 2012, Maui, United States. pp.667-670. ⟨hal-02274695⟩

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