  Leveraging the potential of Virtualised Network Functions (VNFs) requires a
clear understanding of the link between resource consumption and performance.
The current state of the art tries to do that by utilising Machine Learning
(ML) and specifically Supervised Learning (SL) models for given network
environments and VNF types assuming single-objective optimisation targets.
Taking a different approach poses a novel VNF profiler optimising
multi-resource type allocation and performance objectives using adapted
Reinforcement Learning (RL). Our approach can meet Key Performance Indicator
(KPI) targets while minimising multi-resource type consumption and optimising
the VNF output rate compared to existing single-objective solutions. Our
experimental evaluation with three real-world VNF types over a total of 39
study scenarios (13 per VNF), for three resource types (virtual CPU, memory,
and network link capacity), verifies the accuracy of resource allocation
predictions and corresponding successful profiling decisions via a benchmark
comparison between our RL model and SL models. We also conduct a complementary
exhaustive search-space study revealing that different resources impact
performance in varying ways per VNF type, implying the necessity of
multi-objective optimisation, individualised examination per VNF type, and
adaptable online profile learning, such as with the autonomous online learning
approach of iOn-Profiler.
