SCI: SGER: Predicting Batch Queue Waiting Time on ETF Resources

SCI：SGER：预测 ETF 资源的批量队列等待时间

• 批准号: 0526005
• 负责人: Richard Wolski
• 金额: --
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-05-15 至 2007-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0526005&HistoricalAwards=false
• 关键词: SCI; SGER; Predicting; Batch; Queue

This SGER proposes to develop statistical techniques for using current information about the load on given clusters, combine it with the known characteristics of a given user job, and estimate the wait times that would result if the job were submitted on each of the given clusters. The hope is that this will lead to shorter turn-around times for users and more efficient use of the given clusters.Given the rapidly evolving nature of grid computing and the need for developing adequate statistical techniques, this project has considerable risk. The PI's track record to date in related projects gives confidence that the problem is well understood and that the nature of the statistical techniques is well understood. The key mathematical/statistical work will be done by John Brevik, who has collaborated with the PI on related problems in the application of mathematics to grid computing at least since 2001. Longstanding collaborations with the San Diego Supercomputer Center and with the Texas Advanced Computing Center ensure that the techniques will receive current relevant data and will be applied using grid software being developed at the Texas Advanced Computing Center. Thus, the potential for rapid translation of advances in mathematics and statistics to useful software of application to the Extensible Terascale Facility is strong.

该SGER建议开发用于使用关于给定集群上的负载的当前信息的统计技术，将其与给定用户作业的已知特征联合收割机相结合，并估计如果作业在每个给定集群上提交将导致的等待时间。希望这将导致更短的周转时间为用户和更有效地利用给定的集群，鉴于网格计算的迅速发展的性质和发展适当的统计技术的需要，这个项目有相当大的风险。PI迄今为止在相关项目中的跟踪记录使人相信，该问题已得到很好的理解，统计技术的性质也得到很好的理解。关键的数学/统计工作将由John Brevik完成，他至少从2001年开始就与PI合作解决数学应用于网格计算的相关问题。与圣地亚哥超级计算机中心和得克萨斯州高级计算中心的长期合作确保了这些技术将接收当前的相关数据，并将使用得克萨斯州高级计算中心正在开发的网格软件进行应用。因此，将数学和统计方面的进展迅速转化为可扩展太尺度设施的有用应用软件的潜力很大。