Introducing Queuing Network-Based Performance Awareness in Autonomic Systems

Ahmed Harbaoui 1 Nabila Salmi 2 Bruno Dillenseger 2 Jean-Marc Vincent 1
1 MESCAL - Middleware efficiently scalable
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
2 Orange Labs R&D [Rennes]
Abstract : This paper advocates for the introduction of performance awareness in autonomic systems. The motivation is to be able to predict the performance of a target configuration when a self-* feature is planning a system reconfiguration. We propose a global and partially automated process based on queues and queuing networks models. This process includes decomposing a distributed application into black boxes, identifying the queue model for each black box and assembling these models into a queuing network according to the candidate target configuration. Finally, performance prediction is performed either through simulation or analysis. This paper sketches the global process and focuses on the black box model identification step. This step is automated thanks to a load testing platform enhanced with a workload control loop. Model identification is then based on statistical tests. The model identification process is illustrated by experimental results.
Type de document :
Communication dans un congrès
The Sixth International Conference on Autonomic and Autonomous Systems ICAS 2010, 2010, Cancun, Mexico. IEEE, pp.7-12, 2010, 〈10.1109/ICAS.2010.9〉
Liste complète des métadonnées
https://hal.inria.fr/hal-00788865
Contributeur : Arnaud Legrand <>
Soumis le : vendredi 15 février 2013 - 13:09:42
Dernière modification le : jeudi 11 octobre 2018 - 08:48:02

Identifiants

Collections

INRIA | UGA | LIG_SRCPR | LIG

Citation

Ahmed Harbaoui, Nabila Salmi, Bruno Dillenseger, Jean-Marc Vincent. Introducing Queuing Network-Based Performance Awareness in Autonomic Systems. The Sixth International Conference on Autonomic and Autonomous Systems ICAS 2010, 2010, Cancun, Mexico. IEEE, pp.7-12, 2010, 〈10.1109/ICAS.2010.9〉. 〈hal-00788865〉

Exporter

BibTeX TEI DC DCterms EndNote

Partager

Métriques

Consultations de la notice

160

Contact


archive-ouverte@inria.fr