The task scheduling for Remote Sensing Quantitative Retrieval based on hierarchical grid computing platform

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.