NetScope: Fault Localization in Programmable Networking Systems With Low-Cost In-Band Network Telemetry and In-Network Detection
Hongyang Chen, Benran Wang, Guangba Yu, Zilong He, Pengfei Chen, Chen Sun, Zibin Zheng · IEEE Transactions on Networking · 2025
Recently, Software Defined Networking (SDN) has gained widespread adoption as a network infrastructure. Although the openness and programmability of SDN facilitate large complex network construction, diagnosing faults in datacenter-scale network remains challenging. Previous network diagnosis tools pose significant overhead in fine-grained telemetry and typically lack automated fine-grained fault diagnosis capabilities. Although on-demand monitoring methods have been proposed to reduce telemetry overhead, they struggle with effectively setting fixed thresholds, which requires expert experience. This paper presents NetScope, a lightweight system for real-time anomaly detection with self-adaptive thresholds and automatic root cause localization in programmable networking systems. NetScope estimates latency medians for each Flow (i.e., a pair of source and sink switches) within the switch using the proposed per-Flow quantile sketch and calculates the threshold accordingly for anomaly detection. Upon detecting anomalies, NetScope collects aggregated packet-level telemetry on demand and generates a ranked list of fine-grained fault culprits at multiple levels, including port-level, Flow-level, and switch-level. Extensive experiments demonstrate the effectiveness and efficiency of NetScope in anomaly detection and fault localization. Specifically, NetScope achieves a 32%~116% relative improvement in anomaly detection and 6%~197% improvement in root cause analysis compared with other baselines without causing any network bandwidth in anomaly detection while consuming 64.2% less telemetry bandwidth for localization.