The performance of a database system depends strongly on the layout of database objects, such as indexes or tables, onto the underlying storage devices. A good layout will both balance the I/O workload generated by the database system and avoid the performance-degrading interference that can occur when concurrently accessed objects are stored on the same volume. In current practice, layout is typically guided by heuristics and rules of thumb, such as separating indexes and tables or striping all objects across all of the available storage devices. However, these guidelines may give poor results. In this paper, we address the problem of generating an optimized layout of a given set of database objects. Our layout optimizer goes beyond generic guidelines by making use of a description of the database system's I/O activity. We formulate the layout problem as a non-linear programming (NLP) problem and use the I/O description as input to an NLP solver. Our layout optimization technique, which is incorporated into a database layout advisor, identifies a layout that both balances load and avoids interference. We evaluate experimentally the efficacy of our approach and demonstrate that it can quickly identify non-trivial optimized layouts.

Modern storage systems are sophisticated. Simple directattached storage devices are giving way to storage systems that are shared, flexible, virtualized and network-attached. Today, storage systems have their own administrators, who use specialized tools and expertise to configure and manage storage resources. Although the separation of storage management and database management has many advantages, it also introduces problems. Database physical design and storage configuration are closely related tasks, and the separation makes it more difficult to achieve a good end-toend design. In this paper, we attempt to close this gap by addressing the problem of predicting the storage workload that will be generated by a database management system. Specifically, we show how to translate a database workload description, together with a database physical design, into a characterization of the storage workload that will result. Such a characterization can be used by a storage administrator to guide storage configuration. The ultimate goal of this work is to enable effective end-to-end design and configuration spanning both the database and storage system tiers. We present an empirical assessment of the cost of workload prediction as well as the accuracy of the result.