"hStorage-DB: heterogeneity-aware data management to exploit full 
capacity of hybrid storage systems",  

Tian Luo, Rubao Lee, Michael Mesnier, Fen Chen, and Xiaodong Zhang

Proceedings of 38th International Conference on Very Large Databases 
(VLDB 2012), Istanbul, Turkey, August 27-31, 2012.  


Abstract
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.