Accelerating Remote Sensing Data Analysis Workflows by On-demand Processing

In the recent decades, remote sensing data are rapidly growing in size and variety. In the traditional remote sensing analysis workflows, Earth science data users have to download raw data files to local workstations before processing them for science discoveries. The data transfer often costs a lot of time and slows down the analysis workflows. In most cases, results of remote sensing data analysis models are usually much smaller than raw data to be processed. Therefore, "on-demand processing", which tries to upload data analysis models and execute them "near" data, can reduce the costs of data transfer and significantly accelerate the execution of remote sensing analysis workflows. In this paper, a framework, which enables on-demand remote sensing data analysis, is proposed. The evaluation on a prototype system shows that on-demand processing framework obviously accelerates the execution of analysis models by reducing data transfers, especially for those analysis workflows which transfer data through low bandwidth Internet