The STORM approach requires gathering usage data of various database objects such as tables and indices. A journal article about STORM [1] describes non-intrusive techniques for gathering such data in Oracle and SQL-Server. A similar approach is available in other databases such as MySQL, PostGreSQL and DB2. The article also describes how such data can be used to reach a near optimal decision of which database object should be stored in which storage device in a SAN system.
The STORM approach is database and storage system independent. A balancing of database storage utilization across storage devices is simple within the confines of single database system. However, it is very typical in enterprise systems to have multiple heterogeneous databases that share a common pool of SAN stores. In such a scenario, balancing the utilization of storage devices in the SAN system necessarily calls for some sort of a global solution than can work on top of any number of storage and database systems. STORM serves this purpose.
An extensive simulation-based evaluation of STORM revealed that, its employed heuristic converges to an acceptable solution that is successful in balancing storage utilization with an accuracy that lies within generate 7% of the ideal solution. With the TPC-C database benchmark, STORM improved overall performance by as much as 22% by reconfiguring an initial random, but evenly distributed, placement of database objects.
Further reading:
[1] Workload-based Generation of Administrator Hints for Optimizing Database Storage Utilization, Kaushik Dutta, Raju Rangaswami, and Sajib Kundu, ACM Transactions on Storage, 3(4), February, 2008.
A complete version of the article above can be accessed online.
