NDLI: Responsive elastic computing

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Responsive elastic computing

Content Provider	ACM Digital Library
Author	Germain-Renaud, Cécile Loomis, Charles Perez, Julien Kégl, Balázs
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 scientific 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.
Starting Page	55
Ending Page	64
Page Count	10
File Format	PDF
ISBN	9781605585789
DOI	10.1145/1555301.1555311
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2009-06-15
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Grid Reinforcement learning Scheduling
Content Type	Text
Resource Type	Article

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