标题： 对比学习用于关联网络事件 显示英文标题

标题： Contrastive Learning for Correlating Network Incidents

摘要： 互联网服务提供商监控其网络以检测、分类和修复服务中断。 当检测到事件时，确定过去是否发生过类似事件或网络其他地方是否同时发生类似事件非常重要。 由于观察到的网络规模庞大，手动关联此类事件不可行，因此自动化关联成为必要。 本文提出了一种基于相似性的网络状态自监督学习方法。 使用这种方法，通过对比学习在一个大型未标记的网络状态数据集上训练深度神经网络。 在真实网络监控数据上的实验中取得的高精度表明，对比学习是网络事件关联的一种有前景的方法。 显示英文摘要

摘要： Internet service providers monitor their networks to detect, triage, and remediate service impairments. When an incident is detected, it is important to determine whether similar incidents have occurred in the past or are happening concurrently elsewhere in the network. Manual correlation of such incidents is infeasible due to the scale of the networks under observation, making automated correlation a necessity. This paper presents a self-supervised learning method for similarity-based correlation of network situations. Using this method, a deep neural network is trained on a large unlabeled dataset of network situations using contrastive learning. High precision achieved in experiments on real-world network monitoring data suggests that contrastive learning is a promising approach to network incident correlation.