Cache investment: integrating query optimization and distributed data placement
ACM Transactions on Database Systems (TODS)
Vertical Data Migration in Large Near-Line Document Archives Based on Markov-Chain Predictions
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
A Cost-Based Replacement Algorithm for Object Buffers
COMPSAC '00 24th International Computer Software and Applications Conference
Integrated document caching and prefetching in storage hierarchies based on Markov-chain predictions
The VLDB Journal — The International Journal on Very Large Data Bases
A reinforcement learning framework for online data migration in hierarchical storage systems
The Journal of Supercomputing
Dynamic tuning of online data migration policies in hierarchical storage systems using reinforcement learning
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The paper presents a method for distributed caching to exploit the aggregate memory of networks of workstations in data-intensive applications. In contrast to prior work, the approach is based on a detailed cost model as the basis for optimizing the placement of variable-size data objects in a distributed, possibly heterogeneous two-level storage hierarchy. To address the online problem with a priori unknown and evolving workload parameters, the method employs dynamic load tracking procedures and an approximative, low-overhead version of the cost model for continuous reoptimization steps that are embedded in the decisions of the underlying local cache managers. The method is able to automatically find a good tradeoff between an "egoistic" and an "altruistic" behavior of the network nodes, and proves its practical viability in a detailed simulation study under a variety of workload and system configurations.