On performance modeling for the management of Cloud-native Network Functions in closed-loops

Allocating guaranteed resources and optimizing system parameters for Cloud-native Network Function (CNF) deployments introduce significant complexity in management. The performance of CNFs deployed on shared resources always depends on their incoming traffic and their competition for the underlying resource, both of which are usually very dynamic. The complexity of managing their deployments across many physical resources can explode very quickly and become infeasible for taking runtime management decisions in closed-loops. In this work we experimentally analyze the impact of CNFs sharing Central Processing Unit (CPU) and memory resources and make a key observation that increasing the number of deployed CNFs results in an increase of packet service time variability which in turn leads to longer waiting times. We also propose a model of CNF performance when sharing these resources, and discuss its extension to a general compute-network resource model for CNF performance estimation in closed management loops.