The Application of Cloud Computing to the Creation of Image Mosaics and Management of Their Provenance

TL;DR

This paper evaluates the performance and cost of image mosaics processing using Montage on Amazon EC2 cloud versus a high-performance cluster, and explores provenance management for scientific workflows.

Contribution

It provides a comparative analysis of Montage performance on cloud and cluster environments and investigates provenance management technologies for scientific image mosaics.

Findings

Montage performs efficiently on Amazon EC2 for large image mosaics.

Provenance management technologies like PASOA can handle complex scientific workflows.

Cloud-based processing offers a cost-effective alternative to traditional clusters.

Abstract

We have used the Montage image mosaic engine to investigate the cost and performance of processing images on the Amazon EC2 cloud, and to inform the requirements that higher-level products impose on provenance management technologies. We will present a detailed comparison of the performance of Montage on the cloud and on the Abe high performance cluster at the National Center for Supercomputing Applications (NCSA). Because Montage generates many intermediate products, we have used it to understand the science requirements that higher-level products impose on provenance management technologies. We describe experiments with provenance management technologies such as the "Provenance Aware Service Oriented Architecture" (PASOA).