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Cusum techniques for timeslot sequences with applications to network surveillance

机译:用于时隙序列的Cusum技术及其在网络监控中的应用

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摘要

We develop two cusum, change-point detection algorithms for data network monitoring applications where numerous and various performance and reliability metrics are available to aid with the early identification of realized or impending failures. We confront three significant challenges with our cusum algorithms: (1) the need for nonparametric techniques so that a wide variety of metrics can be included in the monitoring process, (2) the need to handle time varying distributions for the metrics that reflect natural cycles in work load and traffic patterns, and (3) the need to be computationally efficient with the massive amounts of data that are available for processing. The only critical assumption we make when developing the algorithms is that suitably transformed observations within a defined timeslot structure are independent and identically distributed under normal operating conditions. To facilitate practical implementations of the algorithms, we present asymptotically valid thresholds. Our research was motivated by a real-world application and we use that context to guide the design of a simulation study that examines the sensitivity of the cusum algorithms.
机译:我们为数据网络监视应用程序开发了两种定制的变化点检测算法,在这些应用程序中,可以使用多种不同的性能和可靠性指标来帮助及早识别已实现或即将发生的故障。我们使用cusum算法面临三个重大挑战:(1)对非参数技术的需求,以便可以在监视过程中包含各种指标,(2)需要处理反映自然周期的指标的时变分布(3)需要具有可用于处理的大量数据的高效计算能力。我们在开发算法时所做的唯一关键假设是,在正常的工作条件下,在定义的时隙结构内进行适当转换的观测值是独立且均等分布的。为了促进算法的实际实现,我们提出了渐近有效阈值。我们的研究受到现实世界应用程序的启发,我们使用该上下文来指导模拟研究的设计,该研究将研究cusum算法的敏感性。

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