Storage architecture includes more and more processing power for increasing requirement of reliability, managibility and scalability. For example, an IBM storage server is equipped with 4 or 8 state-of-the-art processors and gigabytes of memories. This trend enables analyzing data locally inside a storage server. Processing data locally is appealing under the following circumstances: (1) huge reduction of data flowing to the host, (2) reduction of CPU consumption on host. Accordingly, the benefits are (1) less data traffic through IO channel to the host, (2) better utilization of host bufferpool, and (3) enabling more workload on the host. One crucial task is to understand how DBMS can benefit from such hardware. That is to identify which database operations are beneficial to be offloaded given a query workload in a particular setting. For certain operations, we establish value proposition via various approaches and show the analytical and experimental results. In particular, starjoin queries are commonly used in business warehouses. We propose to offload a portion of a starjoin query from host to the POWER5 P processors on a storage server, which dramatically reduces the amount of channel IO and host CPU consumption. Moreover, the query elapsed time is improved via the exploitation of the state-of-the-art P processors on a storage server.

As database systems have grown in terms of scale and complexity, administration tasks have become increasingly difficult and time consuming. A scarcity of skilled database professionals has meant that human costs have begun to dominate the total cost of ownership (TCO) of a database system. Database vendors are under immense pressure to provide solutions that make their products easy to administer in areas such as problem diagnostics, monitoring, query tuning, access control and system configuration.