EAGER: Multi-Domain, Workflow Driven Computation System for Microbial Ecology Research and Analysis

这个热切的奖项授予加州大学圣地亚哥分校，探索社区先进微生物生态研究和分析的社区网络基础设施的创新，(Camera，http://camera.calit2.net/).Camera是一种广泛使用的基础设施，它提供了一个单一的软件系统，用于存储、定位、分析、可视化和共享有关微生物生物学的数据。相机的一个常用和核心组件是用于数据分析的工作流驱动的计算系统。该奖项预计将对下一代序列(NGS)数据进行必要的分析。使用CAMERA的Refseq蛋白质数据库的BLASTx工作流程)将需要近250万个核心小时来完成对典型NGS数据集的分析，这使得整个工作流程依赖单一计算资源是不切实际且成本高昂的。PI将原型基于启发式的动态资源调度方法，允许工作流任务在本地系统、各种商业云和多个国家资源等异质计算域之间分配。该研究探索了一个多域调度系统的开发，其中工作流的单个组件任务不受单个计算资源或域的约束，而是可以跨多个资源和跨机构域进行调度。它使大型和小型实验室中迅速增长的科学界受益，这些实验室的仪器现在以适中的成本产生大量基因组数据，但他们没有足够的机会获得处理或分析这些数据所需的资源。即将建立的网络基础设施可能会被面临类似计算挑战的其他科学界使用。

This EAGER award to the University of California - San Diego, explores innovations to The Community Cyberinfrastructure for Advanced Microbial Ecology Research and Analysis, (CAMERA, http://camera.calit2.net/). CAMERA is a widely used infrastructure that provides a single software system for depositing, locating, analyzing, visualization and sharing data about microbial biology. A heavily utilized and core component of CAMERA is the workflow driven computational system for data analysis. The award anticipates analysis needed for next generation sequence (NGS) data. BLASTx Work flows using CAMERA's Refseq Protein database) will require nearly 2.5 Million core hours to complete analysis of a typical NGS data set, making it impractical and costly to rely on a single computational resource for an entire workflow. The PI will prototype novel heuristic-based dynamic resource scheduling approaches to allowing distribution of workflow tasks among heterogeneous computing domains such as local systems, a variety of commercial clouds, and multiple national resources.This research explores development of a multi-domain scheduling system where the individual component tasks of a workflow are not bound to a single computational resource or domain, but rather can be scheduled across multiple resources as well as across institutional domains. It benefits a rapidly growing segment of the scientific community in both large and modest laboratories whose instruments now generate massive amounts of genomic data at modest expense but who have insufficient access to resources needed for processing or analyzing these data. The cyberinfrastructure to be established will potentially be usable by other science communities facing similar computational challenges.