Spatio-temporal patterns in network events

Ting Wang, M. Srivatsa, D. Agrawal, Ling Liu
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引用次数: 23

Abstract

Operational networks typically generate massive monitoring data that consist of local (in both space and time) observations of the status of the networks. It is often hypothesized that such data exhibit both spatial and temporal correlation based on the underlying network topology and time of occurrence; identifying such correlation patterns offers valuable insights into global network phenomena (e.g., fault cascading in communication networks). In this paper we introduce a new class of models suitable for learning, indexing, and identifying spatio-temporal patterns in network monitoring data. We exemplify our techniques with the application of fault diagnosis in enterprise networks. We show how it can help network management systems (NMSes) to effciently detect and localize potential faults (e.g., failure of routing protocols or network equipments) by analyzing massive operational event streams (e.g., alerts, alarms, and metrics). We provide results from extensive experimental studies over real network event and topology datasets to explore the effcacy of our solution.
网络事件的时空模式
运行网络通常产生大量监测数据,这些数据包括对网络状态的本地(在空间和时间上)观察。通常假设这些数据显示基于底层网络拓扑结构和发生时间的空间和时间相关性;识别这种相关模式提供了对全球网络现象(例如,通信网络中的故障级联)有价值的见解。本文介绍了一类新的模型,适用于网络监测数据中的时空模式的学习、索引和识别。最后以故障诊断在企业网络中的应用为例。我们展示了它如何通过分析大量操作事件流(例如,警报、警报和度量)来帮助网络管理系统(nms)有效地检测和定位潜在故障(例如,路由协议或网络设备的故障)。我们提供了在真实网络事件和拓扑数据集上进行的大量实验研究的结果,以探索我们的解决方案的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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