用对比学习自动关联网络故障，提升运维效率
Contrastive Learning for Correlating Network Incidents
- 通过对比学习训练神经网络，从无标签数据中学习网络状态相似性
- 在真实网络监控数据上实现高精度故障关联
- 适合大规模网络运维人员快速定位重复或并发故障
互联网服务提供商需监控网络以检测、分类和修复服务中断。当发现故障时，判断其是否为历史重现或跨区域并发至关重要。由于网络规模庞大，人工关联不可行，自动化关联成为必要。本文提出一种基于自监督学习的相似性关联方法：利用对比学习，在大规模无标签网络状态数据上训练深度神经网络。实验结果表明，该方法在真实网络监控数据上取得了高精度，证明对比学习是网络故障关联的有前景方案。
原文摘要 · Abstract (English)
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.
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