CSR: Small: Collaborative Research: Enabling Cost-Effective Cloud HPC

CSR：小型：协作研究：实现经济高效的云 HPC

• 批准号: 1318572
• 负责人: Radu Sion
• 金额: $ 15万
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-10-01 至 2017-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1318572&HistoricalAwards=false
• 关键词: CSR; Small; Collaborative; Research; Enabling

The project examines novel services built on top of public cloudinfrastructure to enable cost-effective high-performance computing.The PIs will explore the on-demand, elastic, and configurable features ofcloud computing to complement the traditional supercomputer/clusterplatforms. More specifically, this research aims to assess the efficacyof building dynamic cloud-based clusters leveraging the configurabilityand tiered pricing model of cloud instances. The scientific value of thisproposal lies in the novel use of untapped features of cloud computingfor HPC and the strategic adoption of small, cloud-based clusters forthe purpose of developing/tuning applications for large supercomputers.Through this research, the PIs expect to answer key research questionsregarding: (1) automatic workload-specific cloud cluster configuration,(2) cost-aware and contention-aware data and task co-scheduling,and (3) adaptive, integrated cloud instance provisioning and jobscheduling, plus workload aggregation for cloud instance rental costreduction. If successful, this research will result in tools thatadaptively aggregate, configure, and re-configure cloud resources fordifferent HPC needs, with the purpose of offering low-cost R&Denvironments for scalable parallel applications.

该项目研究了建立在公共云基础设施之上的新型服务，以实现经济高效的高性能计算。PI将探索云计算的按需，弹性和可配置特性，以补充传统的超级计算机/集群平台。更具体地说，本研究旨在评估利用云实例的可配置性和分层定价模型构建动态云集群的效率。该提案的科学价值在于将云计算的未开发功能用于HPC，并战略性地采用基于云的小型集群，以便为大型超级计算机开发/调整应用程序。通过这项研究，PI希望回答以下关键研究问题：（1）自动工作负载特定的云集群配置，（2）成本感知和竞争感知的数据和任务协同调度，以及（3）自适应，集成的云实例配置和作业调度，加上工作负载聚合，可降低云实例租赁成本。如果成功的话，这项研究将导致工具，自适应聚合，配置和重新配置云资源，为不同的HPC需求，目的是提供低成本的R D环境，可扩展的并行应用程序。