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IEEE Transactions on Knowledge and Data Engineering
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
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CLIC: client-informed caching for storage servers
FAST '09 Proccedings of the 7th conference on File and storage technologies
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SieveStore: a highly-selective, ensemble-level disk cache for cost-performance
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Proceedings of the VLDB Endowment
ARC: a self-tuning, low overhead replacement cache
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Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Hystor: making the best use of solid state drives in high performance storage systems
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HPCA '11 Proceedings of the 2011 IEEE 17th International Symposium on High Performance Computer Architecture
I-CASH: Intelligently Coupled Array of SSD and HDD
HPCA '11 Proceedings of the 2011 IEEE 17th International Symposium on High Performance Computer Architecture
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SOSP '11 Proceedings of the Twenty-Third ACM Symposium on Operating Systems Principles
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PACT '13 Proceedings of the 22nd international conference on Parallel architectures and compilation techniques
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FAST'13 Proceedings of the 11th USENIX conference on File and Storage Technologies
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As storage systems become increasingly heterogeneous and complex, it adds burdens on DBAs, causing suboptimal performance even after a lot of human efforts have been made. In addition, existing monitoring-based storage management by access pattern detections has difficulties to handle workloads that are highly dynamic and concurrent. To achieve high performance by best utilizing heterogeneous storage devices, we have designed and implemented a heterogeneity-aware software framework for DBMS storage management called hStorage-DB, where semantic information that is critical for storage I/O is identified and passed to the storage manager. According to the collected semantic information, requests are classified into different types. Each type is assigned a proper QoS policy supported by the underlying storage system, so that every request will be served with a suitable storage device. With hStorage-DB, we can well utilize semantic information that cannot be detected through data access monitoring but is particularly important for a hybrid storage system. To show the effectiveness of hStorage-DB, we have implemented a system prototype that consists of an I/O request classification enabled DBMS, and a hybrid storage system that is organized into a two-level caching hierarchy. Our performance evaluation shows that hStorage-DB can automatically make proper decisions for data allocation in different storage devices and make substantial performance improvements in a cost-efficient way.