论文标题
ESPICE：复杂事件处理中输入事件流的概率负载脱落
eSPICE: Probabilistic Load Shedding from Input Event Streams in Complex Event Processing
论文作者
论文摘要
复杂的事件处理系统处理输入事件流，即时。由于输入事件速率可能超出了系统的功能，并导致违反定义的延迟绑定，因此使用负载脱落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，以便保持定义的延迟界限，并将结果质量的降解最小化。在流处理域中，已经提出了不同的负载脱落策略，但它们主要取决于单个元素的重要性（事件）。但是，随着复杂的事件处理系统执行模式检测，事件的重要性也受相同模式中其他事件的影响。在本文中，我们提出了一个称为ESPICE的负载脱落框架，用于复杂的事件处理系统。 ESPICE取决于构建一个概率模型，该模型了解窗口中事件的重要性。事件在窗口及其类型中的位置用作构建模型的功能。此外，我们提供算法来决定何时开始删除事件以及要丢弃多少事件。此外，我们广泛评估了两个现实世界数据集上的ESPICE的性能。
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.
