Cloud-native 5G core networks generate large volumes of heterogeneous log data across multiple microservice components, making telemetry management a critical operational challenge. Most existing log reduction techniques have not been evaluated on 5G core logs in particular, so the best approach for reducing log volume in such a system remains unclear. This paper investigates five log reduction strategies - LogShrink, Denum, SALO, Drain and Log Preprocessing - applied to an Open5GS deployment on a Kubernetes-in-Docker cluster under ten scenarios (steady-state, bursty traffic, and eight fault injections). The strategies are evaluated across volume reduction, CPU overhead and five system visibility metrics.
The two strategy families (online and offline) operate on different inputs and use separate baselines, so their figures are not directly comparable. Lossless offline strategies (LogShrink and Denum) achieve 83–96% byte reduction with full visibility preservation, with Denum far more resource-efficient than LogShrink. Lossy online strategies (SALO, Log Preprocessing, Drain), on the other hand, reduce real-time log streams by 53–89% at low cluster overhead but significantly reduce fault-signal retention. No single strategy dominates all dimensions simultaneously. The study provides a framework for selecting log reduction strategies in cloud-native 5G deployments based on specific operational constraints. ... Read more