HAL : hal-00491560, version 1

DOI : 10.1007/s10723-010-9161-0

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Journal of Grid Computing 8, 3 (2010) 473-492

Multi-objective reinforcement learning for responsive grids

Julien Perez 1, Cecile Germain-Renaud 1, 2, Balázs Kégl 1, 2, 3, Charles Loomis 3

Grid Observatory Collaboration(s)

(2010)

Grids organize resource sharing, a fundamental requirement of large scientific collaborations. Seamless integration of grids into everyday use requires responsiveness, which can be provided by elastic Clouds, in the Infrastructure as a Service (IaaS) paradigm. This paper proposes a model-free resource provisioning strategy supporting both requirements. Provisioning is modeled as a continuous action-state space, multi-objective reinforcement learning (RL) problem, under realistic hypotheses; simple utility functions capture the high level goals of users, administrators, and shareholders. The model-free approach falls under the general program of autonomic computing, where the incremental learning of the value function associated with the RL model provides the so-called feedback loop. The RL model includes an approximation of the value function through an Echo State Network. Experimental validation on a real data-set from the EGEE grid shows that introducing a moderate level of elasticity is critical to ensure a high level of user satisfaction.

1 :	Laboratoire de Recherche en Informatique (LRI)
	CNRS : UMR8623 – Université Paris XI - Paris Sud
2 :	TAO (INRIA Saclay - Ile de France)
	INRIA – CNRS : UMR8623 – Université Paris XI - Paris Sud
3 :	Laboratoire de l'Accélérateur Linéaire (LAL)
	CNRS : UMR8607 – IN2P3 – Université Paris XI - Paris Sud

Domaine

:

Informatique/Calcul parallèle, distribué et partagé Informatique/Apprentissage Informatique/Modélisation et simulation

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Contributeur : Cecile Germain-Renaud <>

Soumis le : Samedi 12 Juin 2010, 20:08:37

Dernière modification le : Mardi 22 Novembre 2011, 11:34:04