Enabling Large Scale Scientific Computations for Expressed Sequence Tag Sequencing over Grid and Cloud Computing Clusters

Description:

This paper discusses expressed sequence tag sequencing over grid and cloud computing clusters, specifically for biological applications. In this paper, the authors propose a Web service framework for high-level job scheduling that is developed for scientific applications.

Creator(s):
Creation Date: September 2009
Partner(s):
UNT College of Arts and Sciences
Collection(s):
UNT Scholarly Works
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Creator (Author):
Pallickara, Sangmi Lee

Indiana University

Creator (Author):
Pierce, Marlon

Indiana University

Creator (Author):
Dong, Qunfeng

University of North Texas; Indiana University

Creator (Author):
Kong, ChinHua

Indiana University

Publisher Info:
Publisher Name: Springer-Verlag
Place of Publication: [Berlin, Heidelberg]
Date(s):
  • Creation: September 2009
Description:

This paper discusses expressed sequence tag sequencing over grid and cloud computing clusters, specifically for biological applications. In this paper, the authors propose a Web service framework for high-level job scheduling that is developed for scientific applications.

Degree:
Department: Biological Sciences
Note:

Abstract: Computer-intensive biological applications are heavily reliant on the availability of computing resources. Grid based HPC clusters and emerging Cloud computing clusters provide a large scale computing environment for scientific users. However, large scale biological application often involves various types of computational tasks which can benefit from different types of computing clusters. Therefore, a high level job scheduling environment which integrates the Grid style HPC clusters and the Cloud computing clusters and manages jobs accordingly based on the characteristics of the jobs is required. In this paper, the authors propose a Web service framework for high-level job scheduling - Swarm. Swarm is developed for scientific applications that must submit massive number of high-throughput jobs or workflows to highly distributed computing clusters. Swarm allows the users to submit jobs to both Grid HPC and Cloud computing clusters. The Swarm service itself is designed to be extensible, lightweight, and easily installable on a desktop or a small server. As a Web service, derivative services based on Swarm can be straightforwardly integrated with Web portals and science gateways. This paper provides the motivation for this research, the architecture of the Swarm framework, and a performance evaluation of the system prototype.

Physical Description:

10 p.

Language(s):
Subject(s):
Keyword(s): grid computing | cloud computing | bioinformatics | scientific computing | high throughput computing
Source: Eighth International Conference on Parallel Processing and Applied Mathematics (PPAM), 2009, Wroclaw, Poland
Contributor(s):
Partner:
UNT College of Arts and Sciences
Collection:
UNT Scholarly Works
Identifier:
  • ARK: ark:/67531/metadc78330
Resource Type: Paper
Format: Text
Rights:
Access: Public