How KubeSkoop Automates Kubernetes Network Troubleshooting

Background and Challenges

Kubernetes networking is complex and has a steep learning curve. Network virtualization makes fault isolation difficult, leading to frequent connectivity failures during container migration.

KubeSkoop Overview

KubeSkoop is an open‑source diagnostic tool for Kubernetes container networks. It automatically builds source‑to‑destination paths, collects configuration from every network node, leverages eBPF kernel monitoring and cloud‑provider (IaaS) checks, and isolates the root cause of connectivity failures. The tool is designed for users without deep networking expertise.

Supported Components

CNI plugins: Flannel, Calico, Cilium, Terway, etc.

Kubernetes Service discovery and NetworkPolicy mechanisms.

Cloud‑provider network configurations (e.g., security groups, route tables).

Problem Types Addressed

Persistent connectivity failures: ping loss, connection timeouts, DNS resolution errors.

Network jitter: intermittent timeouts, 504 errors, occasional resets, and performance degradation.

Architecture and Workflow

KubeSkoop performs diagnosis in three stages: topology construction, information collection, and link simulation.

Topology construction : Using user‑provided source and destination IPs, the tool queries the Kubernetes API server to retrieve Pods, Nodes, Services, and NetworkPolicy objects. It detects the active CNI plugin and underlying infrastructure, then builds a complete access graph.

Information collection : KubeSkoop dispatches collection tasks to each node in the cluster. It gathers runtime data, protocol‑stack details (routing tables, iptables, IPVS), and cloud metadata via eBPF.

Link simulation : With the collected data, the tool simulates packet forwarding across every hop, validates CNI‑specific behavior, and detects packet loss or mis‑routed paths. Results are presented via a web UI.

Deep Network Monitoring

For jitter‑related issues, KubeSkoop provides pod‑level monitoring based on eBPF. It captures kernel events across drivers, netfilter, and TCP stacks, identifies dozens of abnormal scenarios, and exports metrics to Prometheus. Events are stored in Grafana Loki and can be visualized with the KubeSkoop Inspector.

Extensibility and Implementation Details

The core data collection relies on eBPF with CO‑RE (Compile‑Once‑Run‑Everywhere) to minimize compilation overhead and maximize kernel compatibility. Low‑overhead injection and selective filtering keep memory usage low. Dynamic module loading allows users to enable or disable specific eBPF collectors as needed.

Future Roadmap

Support additional cloud providers and CNI plugins.

Introduce packet‑level tracing and automated root‑cause narrowing for unknown issues.

Release KubeSkoop Analysis for intelligent interpretation of metrics and events.

Extend diagnostics to storage and performance domains.

Add application‑layer visibility for protocols such as HTTP and Redis.

Repository

Source code:

https://github.com/alibaba/kubeskoop