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Bitflow: An In Situ Stream Processing Framework

机译：比特流：原位流处理框架

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摘要

The timely processing of continuous data streams gains increasing importance in a variety of fields. Self-healing systems depend on efficient data analysis to detect problems and apply appropriate counter measures. This paper introduces Bitflow, a stream processing framework optimized for the data analysis tasks in self-healing IT systems. Numerous algorithmic contributions allow to mine monitoring data obtained from critical system components, in order to detect and classify anomalies, and to localize their root cause. These data analysis tasks are traditionally executed on big data processing platforms, which run on dedicated hosts and assume complete ownership over the occupied resources. Bitflow takes a different approach by analyzing the monitoring data directly at its source – i.e., in situ. We exploit the fact that IT systems are usually over-provisioned and a fraction of the computational resources can be allocated for self-healing functionality. Bitflow implements a dynamic modeling approach for dataflow graphs, which adapts to varying environments, such as changing data sources, or system components. Further, we describe Bitflow’s scheduling approach, which determines when it is beneficial to migrate a data analysis process to a remote host. Experimental data from practical data analysis tasks shows the applicability of our scheduling solution.
机译：在各种领域中，及时处理连续数据流变得越来越重要。自我修复系统依靠有效的数据分析来发现问题并采取适当的对策。本文介绍了Bitflow，这是一种针对自愈IT系统中的数据分析任务进行了优化的流处理框架。大量的算法贡献可以挖掘从关键系统组件获得的监视数据，以便检测和分类异常并定位其根本原因。这些数据分析任务通常在大数据处理平台上执行，大数据处理平台在专用主机上运行，​​并对所占用的资源承担完全所有权。比特流采用了一种不同的方法，即直接从源头（即就地）分析监视数据。我们利用了这样一个事实，即IT系统通常会被过度配置，并且可以将一部分计算资源分配给自我修复功能。位流为数据流图实现了动态建模方法，该方法可适应变化的环境，例如变化的数据源或系统组件。此外，我们介绍了Bitflow的调度方法，该方法确定何时将数据分析过程迁移到远程主机是有好处的。来自实际数据分析任务的实验数据表明了我们的调度解决方案的适用性。

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