Abstract

Computational science depends on complex, data intensive applications operating on datasets from a variety of scientific instruments. A major challenge is the integration of data into the scientist's workflow. Recent advances in dynamic, networked cloud resources provide the building blocks to construct reconfiguration, end-to-end infrastructure that can increase scientific productivity, but applications are not taking advantage of them. In our previous work, we introduced Dy-N amo, that enabled CASA scientists to improve the efficiency of their operations and effortlessly leverage capabilities of the cloud resources available to them that previously remained underutilized. However, the provided workflow automation did not satisfy all the operational requirements of CASA. Custom scripts were still in production to manage workflow triggering, while multiple layer2 connections would have to be allocated to maintain …

Date

2020

Authors

George Papadimitriou, Eric Lyons, Cong Wang, Komal Thareja, Ryan Tanaka, Paul Ruth, Juan J Villalobos, Ivan Rodero, Ewa Deelman, Michael Zink, Anirban Mandal

Conference

2020 IEEE/ACM Innovating the Network for Data-Intensive Science (INDIS)

Pages

10-21

Publisher

IEEE