FireCloud, a scalable cloud-based platform for collaborative genome analysis: Strategies for reducing and controlling costs. Birger, C., Hanna, M., Salinas, E., Neff, J., Saksena, G., Livitz, D., Rosebrock, D., Stewart, C., Leshchiner, I., Baumann, A., Voet, D., Cibulskis, K., Banks, E., Philippakis, A., & Getz, G. bioRxiv, November, 2017.
FireCloud, a scalable cloud-based platform for collaborative genome analysis: Strategies for reducing and controlling costs [link]Paper  doi  abstract   bibtex   
FireCloud, one of three NCI Cloud Pilots, is a collaborative genome analysis platform built on a cloud computing infrastructure. FireCloud aims to solve the many challenges presented by the increasingly large data sets and computing requirements employed in cancer research. However, cost uncertainty associated with cloud computing's pay-as-you-go model is proving to be a barrier to adoption of cloud computing. In this paper we present guidelines for optimizing workflows to minimize cost and reduce latency. Our guidelines include: (i) dynamic disk sizing to efficiently utilize virtual disks; (ii) tuned provisioning of virtual machines (VMs) using a performance monitoring tool; (iii) taking advantage of steep price discounts of preemptible VMs; and (iv) utilizing the optimal parallelization of a task's workload.
@article{birger_firecloud_2017,
	title = {{FireCloud}, a scalable cloud-based platform for collaborative genome analysis: {Strategies} for reducing and controlling costs},
	copyright = {© 2017, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NonCommercial-NoDerivs 4.0 International), CC BY-NC-ND 4.0, as described at http://creativecommons.org/licenses/by-nc-nd/4.0/},
	shorttitle = {{FireCloud}, a scalable cloud-based platform for collaborative genome analysis},
	url = {https://www.biorxiv.org/content/early/2017/11/03/209494},
	doi = {10.1101/209494},
	abstract = {FireCloud, one of three NCI Cloud Pilots, is a collaborative genome analysis platform built on a cloud computing infrastructure. FireCloud aims to solve the many challenges presented by the increasingly large data sets and computing requirements employed in cancer research. However, cost uncertainty associated with cloud computing's pay-as-you-go model is proving to be a barrier to adoption of cloud computing. In this paper we present guidelines for optimizing workflows to minimize cost and reduce latency. Our guidelines include: (i) dynamic disk sizing to efficiently utilize virtual disks; (ii) tuned provisioning of virtual machines (VMs) using a performance monitoring tool; (iii) taking advantage of steep price discounts of preemptible VMs; and (iv) utilizing the optimal parallelization of a task's workload.},
	language = {en},
	urldate = {2018-12-04TZ},
	journal = {bioRxiv},
	author = {Birger, Chet and Hanna, Megan and Salinas, Edward and Neff, Jason and Saksena, Gordon and Livitz, Dimitri and Rosebrock, Daniel and Stewart, Chip and Leshchiner, Ignaty and Baumann, Alexander and Voet, Douglas and Cibulskis, Kristian and Banks, Eric and Philippakis, Anthony and Getz, Gad},
	month = nov,
	year = {2017},
	pages = {209494}
}

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