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    • 19. 发明授权
    • Application resource model composition from constituent components
    • 组成部分的应用资源模型组成
    • US09135069B2
    • 2015-09-15
    • US13464386
    • 2012-05-04
    • Henrique AndradeBugra GedikSujay Sunil ParekhKun-Lung WuXiaolan Zhang
    • Henrique AndradeBugra GedikSujay Sunil ParekhKun-Lung WuXiaolan Zhang
    • G06F15/16G06F9/44G06F9/50H04L12/24
    • G06F9/5011H04L41/5035
    • Techniques for composing an application resource model are disclosed. The techniques include obtaining operator-level metrics from an execution of a data stream processing application according to a first configuration, wherein the application is executed by nodes of the data stream processing system and the application includes processing elements comprised of multiple operators, wherein two or more of the operators are combined in a first combination to form a processing element according to the first configuration, generating operator-level resource functions from the first combination of operators based on the obtained operator-level metrics, and generating a processing element-level resource function using the generated operator-level resource functions to predict a model for the processing element formed by a second combination of operators, the processing element-level resource function representing an application resource model usable for predicting characteristics of the application executed according to a second configuration.
    • 公开了组合应用资源模型的技术。 这些技术包括根据第一配置从数据流处理应用的执行获得运营商级度量,其中应用由数据流处理系统的节点执行,并且应用包括由多个运营商组成的处理元件,其中两个或 更多的运营商以第一组合组合以形成根据第一配置的处理元件,基于所获得的运营商级度量从运营商的第一组合生成运营商级资源功能,以及生成处理元件级资源 使用所生成的操作者级资源函数来预测由第二组合运算符形成的处理元素的模型,所述处理元素级资源函数表示可用于根据第二配置来执行的用于预测所述应用的特征的应用资源模型 。
    • 20. 发明授权
    • Method for high-performance data stream processing
    • 高性能数据流处理方法
    • US08949810B2
    • 2015-02-03
    • US12139651
    • 2008-06-16
    • Henrique AndradeBugra GedikKun-Lung Wu
    • Henrique AndradeBugra GedikKun-Lung Wu
    • G06F9/45
    • G06F8/451
    • Techniques for optimizing data stream processing are provided. The techniques include employing a pattern, wherein the pattern facilitates splitting of one or more incoming streams and distributing processing across one or more operators, obtaining one or mote operators, wherein the one or more operators support at least one group-independent aggregation and join operation on one or more streams, generating code, wherein the code facilitates mapping of the application onto a computational infrastructure to enable workload partitioning, using the one or more operators to decompose each of the application into one or more granular components, and using the code to reassemble the one or more granular components into one or more deployable blocks to map the application to a computational infrastructure, wherein reassembling the one or more granular components to map the application to the computational infrastructure optimizes data stream processing of the application.
    • 提供了优化数据流处理的技术。 所述技术包括采用模式,其中所述模式有助于分离一个或多个输入流并且跨越一个或多个运算符分配处理,获得一个或多个运算符,其中所述一个或多个运算符支持至少一个独立于组的聚合和连接操作 在一个或多个流上生成代码,其中所述代码有助于将应用程序映射到计算基础设施上以实现工作负载划分,使用所述一个或多个运算符将每个应用程序分解为一个或多个粒度组件,并使用代码 将所述一个或多个粒状部件重组为一个或多个可部署块以将所述应用映射到计算基础设施,其中重新组装所述一个或多个粒度组件以将所述应用映射到所述计算基础设施来优化所述应用的数据流处理。