iOn-Profiler: intelligent Online multi-objective VNF Profiling with Reinforcement Learning
This addresses the need for efficient and adaptable VNF profiling in network virtualization, offering a domain-specific incremental improvement over existing supervised learning approaches.
The paper tackles the problem of optimizing resource allocation for Virtualised Network Functions (VNFs) by developing iOn-Profiler, a reinforcement learning-based profiler that handles multi-objective optimization of resource consumption and performance, achieving improved accuracy and output rates compared to single-objective methods in experiments with three real-world VNF types across 39 scenarios.
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.