FireCloud, a scalable cloud-based platform for collaborative genome analysis: Strategies for reducing and controlling costs
FireCloud, a scalable cloud-based platform for collaborative genome analysis: Strategies for reducing and controlling costs
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FireCloud，一个可扩展的基于云的协作基因组分析平台：降低和控制成本的策略
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
G. Getz
中科院分区：
文献类型：
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作者：
Chet Birger;M. Hanna;Edward Salinas;J. Neff;G. Saksena;D. Livitz;D. Rosebrock;C. Stewart;I. Leshchiner;Alexander Baumann;Douglas Voet;K. Cibulskis;E. Banks;A. Philippakis;G. Getz
FireCloud是三个NCI Cloud Pilots之一，是一个基于云计算基础设施的协作基因组分析平台。FireCloud旨在解决癌症研究中日益庞大的数据集和计算需求所带来的诸多挑战。然而，与云计算的现收现付模式相关的成本不确定性被证明是采用云计算的障碍。在本文中，我们提出了优化工作流程的指导方针，以最大限度地降低成本和减少延迟。我们的准则包括：（i）动态磁盘大小调整以有效地利用虚拟磁盘;（ii）使用性能监视工具来调整虚拟机（VM）的供应;（iii）利用可抢占VM的大幅价格折扣;以及（iv）利用任务的工作负载的最佳并行化。
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.