云原生5G系统中用于早期故障检测的混合监控

中文摘要 AI 辅助

本文介绍了NetMon的设计、实现与评估，NetMon是一种混合网络监控系统，专为基于Kubernetes的5G分组核心网部署而设计，并特别在爱立信的接入与移动性管理功能（AMF）集群上进行了评估。NetMon结合了基于eBPF的被动内核级流量观测、主动TCP探测和集中式关联分析，以在数秒内检测并定位网络性能下降。评估结果表明，该系统能够检测到低至10毫秒的额外延迟或5%数据包丢失的细微故障，正确地将故障归因于受影响的基础设施组件，并在高达50个模拟用户设备（UE）负载的应用负载下保持此能力。每个Pod的总资源开销为34毫核CPU和45 MiB内存，表明该方法有望在不影响被监控工作负载的情况下进行进一步验证。这种混合方法弥补了现有监控工具中的空白：标准健康检查无法检测部分性能下降，基于抓取的系统会引入数十秒的检测延迟，而纯被动工具无法验证空闲网络路径。通过结合这些互补技术并集中分析，该系统提供了早期检测和故障定位能力，这对于维护云原生5G基础设施中的服务质量至关重要。

英文摘要

This paper presents the design implementation and evaluation of NetMon a hybrid network monitoring system designed for Kubernetes-based 5G packet core deployments specifically evaluated on Ericssons Access and Mobility Management Function AMF clusters NetMon combines eBPF-based passive kernel-level traffic observation with active TCP probing and centralized correlation to detect and localize network degradation within seconds The evaluation results demonstrate that the system detects faults as subtle as 10ms of added latency or 5 packet loss correctly attributes them to the affected infrastructure component and maintains this capability under application loads up to 50 simulated UE load The total resource overhead of 34 millicores CPU and 45 MiB memory per pod suggests that the approach is promising for further validation without impacting the monitored workload The hybrid approach addresses a gap in existing monitoring tools standard health checks cannot detect partial degradation scrape-based systems introduce detection delays measured in tens of seconds and purely passive tools cannot verify idle network paths By combining these complementary techniques and centralizing the analysis the system provides the early detection and fault localization capabilities required for maintaining service quality in cloud-native 5G infrastructure.

发表机构

• Chalmers University of Technology and University of Gothenburg（查尔姆斯理工大学和哥德堡大学）
• Ericsson AB（爱立信公司）

机构由 AI 辅助整理，请以论文原文为准。