Wide area traffic: the failure of Poisson modeling
IEEE/ACM Transactions on Networking (TON)
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Aqueduct: Online Data Migration with Performance Guarantees
FAST '02 Proceedings of the Conference on File and Storage Technologies
A Cost-Model-Based Online Method for Ditributed Caching
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Memory Approaches to Reinforcement Learning in Non-Markovian Domains
Memory Approaches to Reinforcement Learning in Non-Markovian Domains
Reinforcement learning with selective perception and hidden state
Reinforcement learning with selective perception and hidden state
IBM Storage Tank-- A heterogeneous scalable SAN file system
IBM Systems Journal
An Architecture for Lifecycle Management in Very Large File Systems
MSST '05 Proceedings of the 22nd IEEE / 13th NASA Goddard Conference on Mass Storage Systems and Technologies
Dynamic tuning of online data migration policies in hierarchical storage systems using reinforcement learning
A reinforcement learning framework for utility-based scheduling in resource-constrained systems
A reinforcement learning framework for utility-based scheduling in resource-constrained systems
A reinforcement learning approach to dynamic resource allocation
A reinforcement learning approach to dynamic resource allocation
Learning finite-state controllers for partially observable environments
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Global versus local constructive function approximation for on-line reinforcement learning
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Boosting the performance of computing systems through adaptive configuration tuning
Proceedings of the 2009 ACM symposium on Applied Computing
Dynamic adaptation of user migration policies in distributed virtual environments
Dynamic adaptation of user migration policies in distributed virtual environments
FACT: a framework for adaptive contention-aware thread migrations
Proceedings of the 8th ACM International Conference on Computing Frontiers
Effects on performance and energy reduction by file relocation based on file-access correlations
Proceedings of the 2012 Joint EDBT/ICDT Workshops
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Multi-tier storage systems are becoming more and more widespread in the industry. They have more tunable parameters and built-in policies than traditional storage systems, and an adequate configuration of these parameters and policies is crucial for achieving high performance. A very important performance indicator for such systems is the response time of the file I/O requests. The response time can be minimized if the most frequently accessed ("hot") files are located in the fastest storage tiers. Unfortunately, it is impossible to know a priori which files are going to be hot, especially because the file access patterns change over time. This paper presents a policy-based framework for dynamically deciding which files need to be upgraded and which files need to be downgraded based on their recent access pattern and on the system's current state. The paper also presents a reinforcement learning (RL) algorithm for automatically tuning the file migration policies in order to minimize the average request response time. A multi-tier storage system simulator was used to evaluate the migration policies tuned by RL, and such policies were shown to achieve a significant performance improvement over the best hand-crafted policies found for this domain.