Kernel-Level Per-Slice UPF Latency Measurement in Containerised 5G Core Networks
For 5G network operators and researchers, it provides the first empirical characterisation of per-slice UPF latency isolation and timing budgets for AI-driven orchestration.
This paper measures UPF forwarding latency in a containerised 5G core with three slices, finding eMBB 99th percentile latency grows from 574 to 1,243 microseconds under load, URLLC is load-insensitive, and N4 PFCP session modification latency stays below 200 microseconds.
The 5G Core User Plane Function is responsible for packet forwarding, GTP-U decapsulation, and quality of service enforcement for every user data session. How the UPF behaves under simultaneous multi-slice workloads remains empirically uncharacterised in the open literature. Specifically, how its forwarding latency responds to load, how well it isolates one slice from another, and what timing budgets remain available for intelligent control are all open questions. This paper presents a measurement study conducted on a containerised open5GS deployment with three concurrent network slices. We design and implement a namespace-aware TC-BPF instrumentation framework that resolves the fundamental obstacle preventing existing tools from attributing latency observations to individual containerised network functions. We deploy eMBB, URLLC, and mMTC slices with realistic application traffic under light, medium, and heavy load conditions and collect approximately 28 million matched N3 to N6 forwarding delay pairs. The gathered results reveal that eMBB forwarding delay is load-sensitive with the 99th percentile growing from 574 to 1,243 microseconds across load conditions. URLLC delay is load-insensitive, confirming per-UPF process isolation. mMTC exhibits wide-tail TCP behaviour. On this platform, N4 PFCP session modification latency remains consistently below 200 microseconds regardless of data-plane load, suggesting substantial timing headroom within the two-millisecond budget assumed by AI-driven UPF orchestration designs. The instrumentation framework, experiment scripts, and dataset schema are released at https://github.com/MP-Akhil-5G/open5gs-slice-measurement.