Journal:Eleven quick tips for architecting biomedical informatics workflows with cloud computing
| Full article title | Eleven quick tips for architecting biomedical informatics workflows with cloud computing |
|---|---|
| Journal | PLoS Computational Biology |
| Author(s) | Cole, Brian S.; Moore, Jason H. |
| Author affiliation(s) | University of Pennsylvania |
| Primary contact | Email: colebr at upenn dot edu |
| Editors | Ouellette, Francis |
| Year published | 2018 |
| Volume and issue | 14(3) |
| Page(s) | e1005994 |
| DOI | 10.1371/journal.pcbi.1005994 |
| ISSN | 1553-7358 |
| Distribution license | Creative Commons Attribution 4.0 International |
| Website | http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005994 |
| Download | http://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1005994&type=printable (PDF) |
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Abstract
Cloud computing has revolutionized the development and operations of hardware and software across diverse technological arenas, yet academic biomedical research has lagged behind despite the numerous and weighty advantages that cloud computing offers. Biomedical researchers who embrace cloud computing can reap rewards in cost reduction, decreased development and maintenance workload, increased reproducibility, ease of sharing data and software, enhanced security, horizontal and vertical scalability, high availability, a thriving technology partner ecosystem, and much more. Despite these advantages that cloud-based workflows offer, the majority of scientific software developed in academia does not utilize cloud computing and must be migrated to the cloud by the user. In this article, we present 11 quick tips for designing biomedical informatics workflows on compute clouds, distilling knowledge gained from experience developing, operating, maintaining, and distributing software and virtualized appliances on the world’s largest cloud. Researchers who follow these tips stand to benefit immediately by migrating their workflows to cloud computing and embracing the paradigm of abstraction.
Introduction
Cloud computing is the on-demand use of computational hardware, software, and networks provided by a third party.[1] The rise of the internet allowed companies to offer fully internet-based file storage services, including Amazon Web Services’ Simple Storage Service, which launched in 2006.[2] Throughout the past decade, cloud computing has expanded from simple file and object storage to a comprehensive array of on-demand services ranging from bare metal servers and networks to fully managed databases and clusters of computers capable of data processing at a massive scale.[3][4]
Modern cloud computing providers and the customers that utilize their services share responsibility for computer systems, with the cloud provider managing the physical hardware and virtualization software and the consumer utilizing the cloud services to architect workflows which may include applications, databases, systems and networks, storage, web servers, and much more.[5][6] In this way, cloud computing allows users to offload the burden of managing physical systems and focus on building and operating solutions.
References
- ↑ Charlebois, K.; Palmour, N.; Knoppers, B.M. (2016). "The Adoption of Cloud Computing in the Field of Genomics Research: The Influence of Ethical and Legal Issues". PLoS One 11 (10): e0164347. doi:10.1371/journal.pone.0164347. PMC PMC5068798. PMID 27755563. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5068798.
- ↑ Fusaro, V.A.; Patil, P.; Gafni, E. et al. (2011). "Biomedical cloud computing with Amazon Web Services". PLoS Computational Biology 7 (8): e1002147. doi:10.1371/journal.pcbi.1002147. PMC PMC3161908. PMID 21901085. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3161908.
- ↑ Schadt, E.E.; Linderman, M.D.; Sorenson, J. et al. (2011). "Cloud and heterogeneous computing solutions exist today for the emerging big data problems in biology". Nature Reviews Genetics 12 (3): 224. doi:10.1038/nrg2857-c2. PMID 21301474.
- ↑ Muth, T.; Peters, J.; Blackburn, J. et al. (2013). "ProteoCloud: a full-featured open source proteomics cloud computing pipeline". Journal of Proteomics 88: 104–8. doi:10.1016/j.jprot.2012.12.026. PMID 23305951.
- ↑ Grossman, R.L.; White, K.P. (2012). "A vision for a biomedical cloud". Journal of Internal Medicine 271 (2): 122–30. doi:10.1111/j.1365-2796.2011.02491.x. PMID 22142244.
- ↑ Stein, L.D.; Knoppers, B.M.; Campbell, P. (2015). "Data analysis: Create a cloud commons". Nature 523 (7559): 149–51. doi:10.1038/523149a. PMID 26156357.
Notes
This presentation is faithful to the original, with only a few minor changes to presentation. Grammar has been updated for clarity. In some cases important information was missing from the references, and that information was added. The original title uses "architecting" as a verb; we've kept it in the title to reference the original article, but references in in-line text have been changed to "designing."
