Prediction Services for Distributed Computing
Prediction Services for Distributed Computing
复制标题
分布式计算的预测服务
DOI：
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发表时间：
2007
期刊：
影响因子：
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通讯作者：
Warren Smith
中科院分区：
文献类型：
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作者：
Warren Smith
TeraGrid 和 Open Science Grid 等分布式系统的用户可以在许多不同的系统上执行他们的应用程序。我们希望通过提供任务发送到不同系统时何时完成的预测来帮助此类用户或他们使用的网格调度程序选择在何处运行应用程序。我们使用历史信息和基于实例的学习技术来预测文件传输时间、批处理调度程序队列等待时间和应用程序执行时间。我们对来自 TACC lonestar 系统的数据的预测误差是平均文件传输时间的 37%、平均队列等待时间的 115% 和平均执行时间的 72%。我们的方法可以显着降低其他工作负载的预测误差。我们用 Web 服务封装了这些预测技术，使分布式系统的用户以及资源代理和元调度器等工具可以使用预测。
Users of distributed systems such as the TeraGrid and Open Science Grid can execute their applications on many different systems. We wish to help such users, or the grid schedulers they use, select where to run applications by providing predictions of when tasks will complete if sent to different systems. We make predictions of file transfer times, batch scheduler queue wait times, and application execution times using historical information and instance-based learning techniques. Our prediction errors for data from the TACC lonestar system are 37 percent of mean file transfer time, 115 percent for mean queue wait time, and 72 percent of mean execution time. Our approach achieves significantly lower prediction error on other workloads. We have wrapped these prediction techniques with Web services, making predictions available to users of distributed systems as well as tools such as resource brokers and metaschedulers.