Abstract

In this paper we examine the issue of optimizing disk usage and scheduling large‐scale scientific workflows onto distributed resources where the workflows are data‐intensive, requiring large amounts of data storage, and the resources have limited storage resources. Our approach is two‐fold: we minimize the amount of space a workflow requires during execution by removing data files at runtime when they are no longer needed and we demonstrate that workflows may have to be restructured to reduce the overall data footprint of the workflow. We show the results of our data management and workflow restructuring solutions using a Laser Interferometer Gravitational‐Wave Observatory (LIGO) application and an astronomy application, Montage, running on a large‐scale production grid‐the Open Science Grid. We show that although reducing the data footprint of Montage by 48% can be achieved with dynamic data …

Date

October 14, 2025

Authors

Gurmeet Singh, Karan Vahi, Arun Ramakrishnan, Gaurang Mehta, Ewa Deelman, Henan Zhao, Rizos Sakellariou, Kent Blackburn, Duncan Brown, Stephen Fairhurst, David Meyers, G Bruce Berriman, John Good, Daniel S Katz

Journal

Scientific Programming

Volume

15

Issue

4

Pages

249-268

Publisher

IOS Press