eSPICE：复杂事件处理中输入事件流的概率减载
eSPICE: Probabilistic Load Shedding from Input Event Streams in Complex Event Processing
摘要：
复杂事件处理系统在运行中处理输入事件流。由于输入事件速率可能超过系统的能力，并导致违反定义的延迟界限，因此使用甩负荷来丢弃一部分输入事件流。这里的关键问题是要丢弃多少事件和哪些事件，以便保持定义的延迟范围，并最大限度地降低结果质量。在流处理领域，已经提出了不同的减载策略，但它们主要取决于单个元组（事件）的重要性。然而，当复杂事件处理系统执行模式检测时，事件的重要性也受到相同模式中的其他事件的影响。在本文中，我们提出了一个称为eSPICE的复杂事件处理系统的减载框架。eSPICE依赖于构建一个概率模型，该模型可以了解窗口中事件的重要性。事件在窗口中的位置及其类型用作构建模型的特征。此外，我们提供算法来决定何时开始丢弃事件以及丢弃多少事件。此外，我们广泛评估了eSPICE在两个真实世界数据集上的性能。
英文摘要：
Complex event processing systems process the input event streams on-the-fly. Since input event rate could overshoot the system's capabilities and results in violating a defined latency bound, load shedding is used to drop a portion of the input event streams. The crucial question here is how many and which events to drop so the defined latency bound is maintained and the degradation in the quality of results is minimized. In stream processing domain, different load shedding strategies have been proposed but they mainly depend on the importance of individual tuples (events). However, as complex event processing systems perform pattern detection, the importance of events is also influenced by other events in the same pattern. In this paper, we propose a load shedding framework called eSPICE for complex event processing systems. eSPICE depends on building a probabilistic model that learns about the importance of events in a window. The position of an event in a window and its type are used as features to build the model. Further, we provide algorithms to decide when to start dropping events and how many events to drop. Moreover, we extensively evaluate the performance of eSPICE on two real-world datasets.
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