Matching Application Access Patterns to Storage Device Characteristics

Carnegie Mellon University, Dept. ECE Ph.D. Dissertation CMU-PDL-03-109. May 2004.

Jiri Schindler

Electrical and Computer Engineering
Carnegie Mellon University
Pittsburgh, PA 15213


Conventional computer systems have insufficient information about storage device performance characteristics. As a consequence, they utilize the available device resources inefficiently, which, in turn, results in poor application performance. This dissertation demonstrates that a few high-level, device-independent hints encapsulating unique storage device characteristics can achieve significant I/O performance gains without breaking the established abstraction of a storage device as a linear address space of fixed-size blocks. A piece of system software (here referred to as storage manager), which translates application requests into individual I/Os, can automatically match application access patterns to the provided characteristics. This results in more efficient utilization of storage devices and thus improved application performance.

This dissertation (i) identifies specific features of disk drives, disk arrays, and MEMS-based storage devices not exploited by conventional systems, (ii) quantifies the potential performance gains these features offer, and (iii) demonstrates on three different implementations (FFS file system, database storage manager, and disk array logical volume manager) the benefits to the applications using these storage managers. It describes two specific attributes: the access delay boundaries attribute delineates efficient accesses to storage devices and the parallelism attribute exploits the parallelism inherent to a storage device. The two described performance attributes mesh well with existing storage manager data structures, requiring minimal changes to their code. Most importantly, they simplify the error-prone task of performance tuning.

Exposing performance characteristics has the biggest impact on systems with regular access patterns. For example in database systems, when decision support (DSS) and on-line transaction processing (OLTP) workloads run concurrently, DSS experiences a speed up of up to 3X, while OLTP exhibits a 7% speedup. With a single layout taking advantage of access parallelism, a database table can be scanned efficiently in both dimensions. Additionally, scan operations run in time proportional to the amount of query payload; unwanted portions of a table are not touched while scanning at full bandwidth.

KEYWORDS: Storage systems performance, database systems, disk arrays, MEMS-based storage





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