GalaxyCloudRunner: enhancing scalable computing for Galaxy

Author:

Goonasekera Nuwan1,Mahmoud Alexandru2,Chilton John3,Afgan Enis2ORCID

Affiliation:

1. Melbourne Bioinformatics, Faculty of Medicine, Dentistry & Health Sciences, University of Melbourne, Melbourne, VIC 3010, Australia

2. Department of Biology, Johns Hopkins University, Baltimore, MD 21218, USA

3. Department of Biochemistry and Molecular Biology, Penn State University, State College, PA 16801, USA

Abstract

Abstract Summary The existence of more than 100 public Galaxy servers with service quotas is indicative of the need for an increased availability of compute resources for Galaxy to use. The GalaxyCloudRunner enables a Galaxy server to easily expand its available compute capacity by sending user jobs to cloud resources. User jobs are routed to the acquired resources based on a set of configurable rules and the resources can be dynamically acquired from any of four popular cloud providers (AWS, Azure, GCP or OpenStack) in an automated fashion. Availability and implementation GalaxyCloudRunner is implemented in Python and leverages Docker containers. The source code is MIT licensed and available at https://github.com/cloudve/galaxycloudrunner. The documentation is available at http://gcr.cloudve.org/.

Funder

National Institutes of Health

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

Reference11 articles.

1. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update;Afgan,2018

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3. Genomics virtual laboratory: a practical bioinformatics workbench for the cloud;Afgan;PLoS One,2015

4. CloudLaunch: discover and deploy cloud applications;Afgan;Fut. Gener. Comput. Syst,2019

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