Profile-based Data-driven Approach to Analyse Virtualised Network Functions Performance

Current Network Function Virtualisation (NFV) orchestration frameworks lack intelligence and handle resources in a reactive manner while neglecting Virtualised Network Function (VNF)-level service performance. This article introduces a novel NFV analysis framework and methodology, which is able to operate in conjunction with already standardised or forthcoming, Artificial Intelligence based VNF management processes. This framework comprises a profile-based data-driven method for the analysis of VNF-level service performance. The novel potential of the proposed method lies in the fact that instead of providing and working with some volatile monitoring metrics for reactive service management, we analyse the impact of the underlying virtualised system’s resource configurations and each VNF’s input data rate, on the performance characteristics of that VNF and its resource utilisation. This will help network operators by providing insights on the resource utilisation and performance behaviour of a VNF to make proactive and efficient resource management plans to meet the targeted service performance. For the evaluation of our proposed approach, an autonomous profiling method is used to perform benchmarking and monitoring and generate real profile information of VNFs in a real deployment environment