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建议开发用于使用有关给定群集上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相结合，并估算如果在每个给定集群上提交作业，则会导致等待时间。希望这将导致用户的转折时间和更有效地使用给定群集。启动网格计算的快速发展的性质以及开发足够的统计技术的需求，该项目具有相当大的风险。迄今为止，PI在相关项目中的往绩使问题充满信心，并且统计技术的性质已得到充分理解。关键的数学/统计工作将由约翰·布雷维克（John Brevik）完成，他与PI至少至少自2001年以来与PI合作，就数学应用于网格计算的相关问题。至少自2001年以来，与圣地亚哥超级计算机中心的长期合作以及德克萨斯州高级计算中心的长期合作，可确保使用当前的相关数据并使用Grid Incelping Computect in Texas Computing Computing Computing Computing Computing Computect。因此，将数学和统计量的进步快速转换为有用的Tecspormensible Terascale设施的有用软件的潜力很强。