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

该SGER建议开发统计技术，以使用有关给定集群上负载的当前信息，将其与给定用户作业的已知特征结合起来，并估计在每个给定集群上提交作业所导致的等待时间。希望这将缩短用户的周转时间，并更有效地使用给定的集群。考虑到网格计算快速发展的本质和开发适当的统计技术的需要，这个项目有相当大的风险。PI迄今在相关项目中的跟踪记录使人们相信，问题已经得到了很好的理解，统计技术的性质也得到了很好的理解。关键的数学/统计工作将由约翰·布雷维克（John Brevik）完成，他至少从2001年起就与PI合作研究网格计算中数学应用的相关问题。与圣地亚哥超级计算机中心和德克萨斯高级计算中心的长期合作确保这些技术将接收当前的相关数据，并将使用德克萨斯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cale设施的有用软件的潜力是很大的。

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.