GalaxyCloudRunner: enhancing scalable computing for Galaxy
- Resource Type
- Authors
- Enis Afgan; John Chilton; Nuwan Goonasekera; Alexandru Mahmoud
- Source
- Bioinformatics
- Subject
- Statistics and Probability
Service (systems architecture)
Source code
Computer science
media_common.quotation_subject
Cloud computing
Documentation
02 engineering and technology
computer.software_genre
Azure Stains
Biochemistry
Set (abstract data type)
03 medical and health sciences
Software
Server
0202 electrical engineering, electronic engineering, information engineering
Humans
Scalable computing
Molecular Biology
030304 developmental biology
computer.programming_language
media_common
0303 health sciences
business.industry
Computational Biology
Python (programming language)
Applications Notes
Galaxy
Computer Science Applications
Computational Mathematics
Computational Theory and Mathematics
Operating system
020201 artificial intelligence & image processing
business
computer
Computer network
- Language
- ISSN
- 1367-4811
1367-4803
SummaryThe 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 4 popular cloud providers (AWS, Azure, GCP, or OpenStack) in an automated fashion.Availability and implementationGalaxyCloudRunner 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/.ContactEnis Afgan (enis.afgan@jhu.edu)Supplementary informationNone