Name
Title | ||
|---|---|---|
The task scheduling for Remote Sensing Quantitative Retrieval based on hierarchical grid computing platform |
Abstract | ||
|---|---|---|
The Remote Sensing Quantitative Retrieval is not only a Compute-intensive problem, but also a Data-intensive problem. One of the most effective solutions is Grid Computing platform built on the basis of network, which is a scalable virtual unified platform with unlimited computing power and storage capacity. The RSSN (Remote Sensing Information Service Grid Node) is a PC cluster for Remote Sensing Quantitative Retrieval. It was used for producing aerosol optical depth (AOD) production covered the land area of Asia. We designed the cluster as a hierarchical one, and achieved a Global Task Scheduler for the workflow process, improved the system performance to some extent. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/IGARSS.2012.6351380 | Geoscience and Remote Sensing Symposium |
Keywords | Field | DocType |
aerosols,geophysical techniques,geophysics computing,grid computing,remote sensing,Asia,RSSN,aerosol optical depth production,compute-intensive problem,data-intensive problem,global task scheduler,hierarchical grid computing platform,remote sensing information service grid node,remote sensing quantitative retrieval,scalable virtual unified platform,storage capacity,Grid Computing,Remote Sensing,five,four,three | Optical depth,Grid computing,Computer science,Scheduling (computing),Remote sensing,Workflow,Service grid,Scalability,Distributed computing | Conference |
Volume | Issue | ISSN |
null | null | 2153-6996 E-ISBN : 978-1-4673-1158-8 |
ISBN | Citations | PageRank |
978-1-4673-1158-8 | 0 | 0.34 |
References | Authors | |
3 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ziqiang Chen | 1 | 12 | 3.41 |
| Yong Xue | 2 | 118 | 57.61 |
| Jing Dong | 3 | 4 | 3.68 |
| Jia Liu | 4 | 10 | 5.97 |
| Yingjie Li | 5 | 45 | 15.79 |