James Alan Ruddy

Integration of Server, Storage and Database Stack: Moving Processing Towards Data

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.

An architecture for storage-hosted application extensions

The very rapid growth of data-intensive computing makes it attractive to perform computations locally, where data is stored. Large storage systems based on standard system technologies with server virtualization capabilities make it feasible to deploy application-specific processing onto the storage system, without jeopardizing the availability of the core storage service or degrading performance. Moreover, price and capacity differences between mainframes and these storage systems make this deployment attractive. We describe the design of a prototype system by which the IBM DS8000™ storage system can host application extensions, called adjuncts, that improve the operation of IBM z/OS® (mainframe) applications. These extensions process large amounts of data in operations such as searching, sorting, and indexing so that the host application need not even access most of the data. The benefits of application extensions result from applying system resources more efficiently. Application processing at the storage system magnifies the total throughput that can be achieved by the host application. Furthermore, by avoiding the transmission of large volumes of data through multiple hardware and software layers, processing often takes a shorter time at a lower cost.