Galaxy CloudMan: delivering cloud compute clusters

• Published in: BMC bioinformatics Vol. 11 Suppl 12; no. S12; p. S4
• Main Authors: Afgan, Enis, Baker, Dannon, Coraor, Nate, Chapman, Brad, Nekrutenko, Anton, Taylor, James
• Format: Journal Article
• Language: English
• Published: England BioMed Central 21.12.2010; BioMed Central Ltd
• Subjects: Cloud computing; Cluster Analysis; Computational Biology - methods; Computer science; Deoxyribonucleic acid; DNA; Grants; Infrastructure; Internet; Life sciences; Linux; Proceedings; Software; User interface
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/1471-2105-11-s12-s4

Widespread adoption of high-throughput sequencing has greatly increased the scale and sophistication of computational infrastructure needed to perform genomic research. An alternative to building and maintaining local infrastructure is "cloud computing", which, in principle, offers on demand access to flexible computational infrastructure. However, cloud computing resources are not yet suitable for immediate "as is" use by experimental biologists. We present a cloud resource management system that makes it possible for individual researchers to compose and control an arbitrarily sized compute cluster on Amazon's EC2 cloud infrastructure without any informatics requirements. Within this system, an entire suite of biological tools packaged by the NERC Bio-Linux team (http://nebc.nerc.ac.uk/tools/bio-linux) is available for immediate consumption. The provided solution makes it possible, using only a web browser, to create a completely configured compute cluster ready to perform analysis in less than five minutes. Moreover, we provide an automated method for building custom deployments of cloud resources. This approach promotes reproducibility of results and, if desired, allows individuals and labs to add or customize an otherwise available cloud system to better meet their needs. The expected knowledge and associated effort with deploying a compute cluster in the Amazon EC2 cloud is not trivial. The solution presented in this paper eliminates these barriers, making it possible for researchers to deploy exactly the amount of computing power they need, combined with a wealth of existing analysis software, to handle the ongoing data deluge.