A parallel network that learns to play backgammon
Artificial Intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
RTAS '01 Proceedings of the Seventh Real-Time Technology and Applications Symposium (RTAS '01)
Utility Functions in Autonomic Systems
ICAC '04 Proceedings of the First International Conference on Autonomic Computing
The Anatomy of the Grid: Enabling Scalable Virtual Organizations
International Journal of High Performance Computing Applications
Reinforcement Learning in Continuous Time and Space
Neural Computation
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Efficient scheduling of soft real-time applications on multiprocessors
Journal of Embedded Computing - Real-Time Systems (Euromicro RTS-03)
Analysis and modeling of job arrivals in a production grid
ACM SIGMETRICS Performance Evaluation Review
Grid Differentiated Services: A Reinforcement Learning Approach
CCGRID '08 Proceedings of the 2008 Eighth IEEE International Symposium on Cluster Computing and the Grid
Feedback-controlled resource sharing for predictable eScience
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
Utility-Based Reinforcement Learning for Reactive Grids
ICAC '08 Proceedings of the 2008 International Conference on Autonomic Computing
Grid Deployment of Legacy Bioinformatics Applications with Transparent Data Access
GRID '06 Proceedings of the 7th IEEE/ACM International Conference on Grid Computing
Reinforcement learning with echo state networks
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
A survey of multi-objective sequential decision-making
Journal of Artificial Intelligence Research
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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.