Hybrid monitoring detects subtle network faults in 5G systems
Hybrid Monitoring for Early Fault Detection in Cloud-Native 5G Systems
Networking and Internet ArchitectureDistributed, Parallel, and Cluster Computing
Summary
Detecting small network problems early in 5G systems is hard but important to keep connections fast and reliable. The authors created NetMon, a tool that watches network traffic in different ways at once to catch tiny delays or lost packets within seconds. It tests parts of the system actively while also observing passively, then combines these views to find exactly where the problem is. Their tests showed NetMon works well without slowing down real users or requiring much extra computing power. This approach fills gaps left by other monitoring tools and helps maintain good service quality in cloud-based 5G networks.
What this means in practice
- •For cloud network operators: Identify and locate small and early network performance degradations in 5G Kubernetes clusters before user experience is affected.
- •For telecommunications engineers: Integrate hybrid monitoring to improve fault detection and diagnosis in 5G packet core network functions at scale.
Authors
Anton Andersson, Sai Akshara Naineni, Mats Jansborg, Yixing Zhang, Romaric Duvignau
Abstract
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.