Aether: Leveraging Linear Programming For Optimal Cloud Computing In Genomics

Citation:

Jacob M. Luber, Braden T. Tierney, Evan M. Cofer, Chirag J. Patel, and Aleksandar D. Kostic. 2017. “Aether: Leveraging Linear Programming For Optimal Cloud Computing In Genomics.” Bioinformatics, btx787. Publisher's Version
btx787.pdf259 KB

Abstract:

Motivation

Across biology we are seeing rapid developments in scale of data production without a corresponding increase in data analysis capabilities.

Results

Here, we present Aether (http://aether.kosticlab.org), an intuitive, easy-to-use, cost-effective, and scalable framework that uses linear programming (LP) to optimally bid on and deploy combinations of underutilized cloud computing resources. Our approach simultaneously minimizes the cost of data analysis and provides an easy transition from users’ existing HPC pipelines.

Availability

Data utilized are available at https://pubs.broadinstitute.org/diabimmune and with EBI SRA accession ERP005989. Source code is available at (https://github.com/kosticlab/aether). Examples, documentation, and a tutorial are available at (http://aether.kosticlab.org).

Contact

chirag_patel@hms.harvard.edu and aleksandar.kostic@joslin.harvard.edu

Last updated on 12/08/2017