hSPICE：复杂事件处理中的状态感知事件脱落
hSPICE: State-Aware Event Shedding in Complex Event Processing
摘要：
在复杂事件处理（CEP）中，当存在对资源的限制时，执行甩负荷以在过载情况期间维持给定的延迟界限。然而，甩负荷意味着结果质量（QoR）的下降。因此，以对QoR影响最小的方式执行甩负荷至关重要。CEP领域的研究人员建议在过载情况下丢弃事件或部分匹配（PM）。他们通过考虑事件的重要性或PM的重要性而不是两者同时考虑来为事件或PM分配效用。在本文中，我们提出了一个切负荷CEP系统的方法，结合这些方法分配一个公用事业的事件，考虑事件的重要性和PM的重要性。我们采用了一个概率模型，该模型使用窗口中事件的类型和位置以及PM的状态来为每个PM对应的事件分配效用。我们还提出了一种预测实用阈值的方法，该实用阈值用于丢弃所需的事件量以保持给定的延迟范围。通过对两个真实数据集和几个代表性查询的广泛评估，我们表明，在大多数情况下，我们的甩负荷方法优于最先进的甩负荷方法，w.r.t. QoR。
英文摘要：
In complex event processing (CEP), load shedding is performed to maintain a given latency bound during overload situations when there is a limitation on resources. However, shedding load implies degradation in the quality of results (QoR). Therefore, it is crucial to perform load shedding in a way that has the lowest impact on QoR. Researchers, in the CEP domain, propose to drop either events or partial matches (PMs) in overload cases. They assign utilities to events or PMs by considering either the importance of events or the importance of PMs but not both together. In this paper, we propose a load shedding approach for CEP systems that combines these approaches by assigning a utility to an event by considering both the event importance and the importance of PMs. We adopt a probabilistic model that uses the type and position of an event in a window and the state of a PM to assign a utility to an event corresponding to each PM. We, also, propose an approach to predict a utility threshold that is used to drop the required amount of events to maintain a given latency bound. By extensive evaluations on two real-world datasets and several representative queries, we show that, in the majority of cases, our load shedding approach outperforms state-of-the-art load shedding approaches, w.r.t. QoR.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
