To Share, or not to Share Online Event Trend Aggregation Over Bursty Event Streams
To Share, or not to Share Online Event Trend Aggregation Over Bursty Event Streams
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DOI：
10.1145/3448016.3452785
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发表时间：
2021-01
期刊：
影响因子：
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通讯作者：
Olga Poppe;Chuan Lei;L. Ma;Allison Rozet;Elke A. Rundensteiner
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作者：
Olga Poppe;Chuan Lei;L. Ma;Allison Rozet;Elke A. Rundensteiner
复杂事件处理(CEP)系统在严格的时间约束下持续评估模式查询的大量工作负载。带有Kleene模式的事件趋势聚合查询通常用于检索有关事件流中最新趋势的汇总见解。最先进的方法由于重复计算或不必要的趋势构建而受到限制。现有的共享方法是由静态选择的、因此是严格的共享计划来指导的，这些共享计划在流量波动下往往是次优的。在这项工作中，我们提出了一个新的框架哈姆雷特，它是第一个克服这些限制的框架。哈姆雷特介绍了两项关键创新。首先，Hamlet在运行时根据当前的流属性自适应地决定是否共享计算，以获取最大的共享收益。第二，哈姆雷特配备了高效的共享趋势聚合策略，避免了趋势构建。我们在真实数据集和合成数据集上的实验研究表明，与最先进的方法相比，Hamlet始终将查询延迟降低了多达五个数量级。
Complex event processing (CEP) systems continuously evaluate large workloads of pattern queries under tight time constraints. Event trend aggregation queries with Kleene patterns are commonly used to retrieve summarized insights about the recent trends in event streams. State-of-art methods are limited either due to repetitive computations or unnecessary trend construction. Existing shared approaches are guided by statically selected and hence rigid sharing plans that are often sub-optimal under stream fluctuations. In this work, we propose a novel framework Hamlet that is the first to overcome these limitations. Hamlet introduces two key innovations. First, Hamlet adaptively decides at run time whether to share or not to share computations depending on the current stream properties to harvest the maximum sharing benefit. Second, Hamlet is equipped with a highly efficient shared trend aggregation strategy that avoids trend construction. Our experimental study on both real and synthetic data sets demonstrates that Hamlet consistently reduces query latency by up to five orders of magnitude compared to state-of-the-art approaches.