Responsive Elastic Computing

Julien Perez 1 C. Germain Renaud 1, 2 Balázs Kégl 1, 2, 3 Charles Loomis 2
3 TAO - Machine Learning and Optimisation
CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LRI - Laboratoire de Recherche en Informatique
Abstract : Two production models are candidates for e-science computing: grids enable hardware and software sharing; clouds propose dynamic resource provisioning (elastic computing). Organized sharing is a fundamental requirement for large scientic collaborations; responsiveness, the ability to provide good response time, is a fundamental requirement for seamless integration of the large scale computing resources into everyday use. This paper focuses on a model-free resource provisioning strategy supporting both scenarios. The provisioning problem is modeled as a continuous action-state space, multi-objective reinforcement learning problem, under realistic hypotheses; the high level goals of users, administrators, and shareholders are captured through simple utility functions. We propose an implementation of this reinforcement learning framework, including an approximation of the value function through an Echo State Network, and we validate it on a real dataset.
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Julien Perez, C. Germain Renaud, Balázs Kégl, Charles Loomis. Responsive Elastic Computing. 2009 ACM/IEEE Conference on International Conference on Autonomic Computing, Jun 2009, Barcelone, Spain. pp.55-64, ⟨10.1145/1555301.1555311⟩. ⟨inria-00384970⟩

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