Data stream management systems (DSMSs) are scalable, highly available, and fault-tolerant systems that aggregate and analyze real-time data in motion. To continuously perform analytics on the fly within the stream, state-of-the-art DSMSs host streaming applications as a set of interconnected operators, with each operator encapsulating the semantic of a specific operation. For parallel execution on a particular platform, these operators need to be appropriately replicated in multiple instances that split and process the workload simultaneously. Because the way operators are partitioned affects the resulting performance of streaming applications, it is essential for DSMSs to have a method to compare different operators and make holistic replication decisions to avoid performance bottlenecks and resource wastage. To this end, we propose a stepwise profiling approach to optimize application performance on a given execution platform. It automatically scales distributed computations over streams based on application features and processing power of provisioned resources and builds the relationship between provisioned resources and application performance metrics to evaluate the efficiency of the resulting configuration. Experimental results confirm that the proposed approach successfully fulfills its goals with minimal profiling overhead.

中文翻译：

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一种用于流应用程序性能优化的逐步自动分析方法

数据流管理系统 (DSMS) 是可扩展、高度可用和容错的系统，可聚合和分析动态的实时数据。为了在流中持续动态地执行分析，最先进的 DSMS 将流应用程序托管为一组互连的运算符，每个运算符都封装了特定操作的语义。对于特定平台上的并行执行，这些算子需要在多个实例中适当地复制，同时拆分和处理工作负载。由于运算符的分区方式会影响流式应用程序的最终性能，因此 DSMS 必须有一种方法来比较不同的运算符并做出整体复制决策，以避免性能瓶颈和资源浪费。为此，我们提出了一种逐步分析方法来优化给定执行平台上的应用程序性能。它根据应用程序特性和预置资源的处理能力自动扩展流上的分布式计算，并在预置资源和应用程序性能指标之间建立关系，以评估结果配置的效率。实验结果证实，所提出的方法以最小的分析开销成功地实现了其目标。它根据应用程序特性和预置资源的处理能力自动扩展流上的分布式计算，并在预置资源和应用程序性能指标之间建立关系，以评估结果配置的效率。实验结果证实，所提出的方法以最小的分析开销成功地实现了其目标。它根据应用程序特性和预置资源的处理能力自动扩展流上的分布式计算，并在预置资源和应用程序性能指标之间建立关系，以评估结果配置的效率。实验结果证实，所提出的方法以最小的分析开销成功地实现了其目标。