Abstract
More and more parallel applications are running in a distributed environment to take advantage of easily available and inexpensive commodity resources. For data intensive applications, employing multiple distributed storage resources has many advantages. In this paper, we present a Multi-Storage I/O System (MS-I/O) that cannot only effectively manage various distributed storage resources in the system, but also provide novel high performance storage access schemes. MS-I/O employs many state-of-the-art I/O optimizations such as collective I/O, asynchronous I/O etc. and a number of new techniques such as data location, data replication, subfile, superfile and data access history. In addition, many MS-I/O optimization schemes can work simultaneously within a single data access session, greatly improving the performance. Although I/O optimization techniques can help improve performance, it also complicates I/O system. In addition, most optimization techniques have their limitations. Therefore, selecting accurate optimization policies requires expert knowledge which is not suitable for end users who may have little knowledge of I/O techniques. So the task of I/O optimization decision should be left to the I/O system itself, that is, automatic from user's point of view. We present a User Access Pattern data structure which is associated with each dataset that can help MS-I/O easily make accurate I/O optimization decisions.
Original language | English (US) |
---|---|
Pages (from-to) | 1623-1643 |
Number of pages | 21 |
Journal | Parallel Computing |
Volume | 29 |
Issue number | 11-12 SPEC.ISS. |
DOIs | |
State | Published - Nov 1 2003 |
Keywords
- Access pattern
- Data intensive computing
- Multi-storage I/O system
ASJC Scopus subject areas
- Software
- Theoretical Computer Science
- Hardware and Architecture
- Computer Networks and Communications
- Computer Graphics and Computer-Aided Design
- Artificial Intelligence