By Topic

A distributed multi-storage resource architecture and I/O performance prediction for scientific computing

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
X. Shen ; Dept. of Electr. & Comput. Eng., Northwestern Univ., Evanston, IL, USA ; A. Choudhary

I/O-intensive applications have posed great challenges to computational scientists. A major problem of these applications is that users have to sacrifice performance requirements in order to satisfy storage capacity requirements in a conventional computing environment. Further performance improvement is impeded by the physical nature of these storage media, even if state-of-the-art I/O optimizations are employed. In this paper, we present a distributed multi-storage resource architecture that can satisfy both performance and capacity requirements by employing multiple storage resources. Compared to the traditional single-storage resource architecture, our architecture provides a more flexible and reliable computing environment. It can bring new opportunities for high-performance computing as well as inheriting state-of-the-art I/O optimization approaches that have already been developed. We also develop an application programming interface (API) that provides transparent management and access to various storage resources in our computing environment. As I/O usually dominates the performance in I/O-intensive applications, we establish an I/O performance prediction mechanism which consists of a performance database and a prediction algorithm to help users better evaluate and schedule their applications. A tool is also developed to help users automatically generate the performance database. Experiments show that our multi-storage resource architecture is a promising platform for high-performance distributed computing

Published in:

High-Performance Distributed Computing, 2000. Proceedings. The Ninth International Symposium on

Date of Conference: