Internet traffic: periodicity, tail behavior, and performance implications
System performance evaluation
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
A Reinforcement Learning Framework for Dynamic Resource Allocation: First Results.
ICAC '05 Proceedings of the Second International Conference on Automatic Computing
Exploration and apprenticeship learning in reinforcement learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
A Hybrid Reinforcement Learning Approach to Autonomic Resource Allocation
ICAC '06 Proceedings of the 2006 IEEE International Conference on Autonomic Computing
Online resource allocation using decompositional reinforcement learning
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Accelerating reinforcement learning through implicit imitation
Journal of Artificial Intelligence Research
Workstation capacity tuning using reinforcement learning
Proceedings of the 2007 ACM/IEEE conference on Supercomputing
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Reinforcement Learning (RL) holds particular promise in an emerging application domain of performance management of computing systems. In recent work, online RL yielded effective server allocation policies in a prototype Data Center, without explicit system models or built-in domain knowledge. This paper presents a substantially improved and more practical “hybrid” approach, in which RL trains offline on data collected while a queuing-theoretic policy controls the system. This approach avoids potentially poor performance in live online training. Additionally we use nonlinear function approximators instead of tabular value functions; this greatly improves scalability, and surprisingly, eliminated the need for exploratory actions. In experiments using both open-loop and closed-loop traffic as well as large switching delays, our results show significant performance improvement over state-of-art queuing model policies.