Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Utility Functions in Autonomic Systems
ICAC '04 Proceedings of the First International Conference on Autonomic Computing
A Reinforcement Learning Framework for Dynamic Resource Allocation: First Results.
ICAC '05 Proceedings of the Second International Conference on Automatic Computing
Utility-Function-Driven Resource Allocation in Autonomic Systems
ICAC '05 Proceedings of the Second International Conference on Automatic Computing
On the convergence of stochastic iterative dynamic programming algorithms
Neural Computation
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
A reinforcement learning framework for online data migration in hierarchical storage systems
The Journal of Supercomputing
Improving scheduling performance using a q-learning-based leasing policy for clouds
Euro-Par'12 Proceedings of the 18th international conference on Parallel Processing
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This paper presents a general framework for performing adaptive reconfiguration of a distributed system based on maximizing the long-term business value, defined as the discounted sum of all future rewards and penalties. The problem of dynamic resource allocation among multiple entities sharing a common set of resources is used as an example. A specific architecture (DRA-FRL) is presented, which uses the emerging methodology of reinforcement learning in conjunction with fuzzy rulebases to achieve the desired objective. This architecture can work in the context of existing resource allocation policies and learn the values of the states that the system encounters under these policies. Once the learning process begins to converge, the user can allow the DRA-FRL architecture to make some additional resource allocation decisions or override the ones suggested by the existing policies so as to improve the long-term business value of the system. The DRA-FRL architecture can also be deployed in an environment without any existing resource allocation policies. An implementation of the DRA-FRL architecture in Solaris 10 demonstrated a robust performance improvement in the problem of dynamically migrating CPUs and memory blocks between three resource partitions so as to match the stochastically changing workload in each partition, both in the presence and in the absence of resource migration costs.