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QEScalor: Quantitative Elastic Scaling Framework in Distributed Streaming Processing

机译：QEScalor：分布式流处理中的定量弹性扩展框架

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摘要

Recently, researchers usually use the elastic scaling techniques as a powerful means of the distributed stream processing systems to deal with the high-speed data stream which arrives continuously and fluctuates constantly. The existing methods allocate the same amount of resources to the instances of the same operator, but they ignore the correlation between the operator performance and resource provision. It may lead to the waste of the resources caused by the over-provision or the huge overhead of the scheduling caused by the under-provision. To solve the above problems, we present a quantitative elastic scaling framework, named QEScalor, to allocate resources for the operator instances quantitatively based on the actual performance requirements. The experimental results show that compared with the existing works, the QEScalor can not only achieve resource-efficient elastic scaling with lower cost, but also it can enhance the total performance of the DSPAs.
机译：最近，研究人员通常将弹性缩放技术用作分布式流处理系统的强大手段，以处理连续到达且不断波动的高速数据流。现有方法将相同数量的资源分配给同一运算符的实例，但是它们忽略了运算符性能与资源提供之间的相关性。这可能会导致因超额配置而造成的资源浪费，或因不足的配置而导致调度的巨大开销。为了解决上述问题，我们提出了一个定量的弹性伸缩框架，称为QEScalor，可以根据实际性能要求为操作员实例定量分配资源。实验结果表明，与现有工作相比，QEScalor不仅可以以较低的成本实现资源高效的弹性缩放，而且可以提高DSPA的整体性能。

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