A Hybrid Machine Learning/Policy Approach to Optimise Video Path Selection

Services such as interactive video and real time gaming are ubiquitous on modern networks. The approaching realisation of 5G as well as the virtualisation and scalability of network functions made possible by technologies such as NFV and Kubernetes pushes the frontiers of what applications can do and how they can be deployed. However, managing such intangible services is a real challenge for network management systems. Adaptive Policy is an approach that can be applied to govern such services in an intent-based manner.In this work, we are exploring if the manner in which such services are deployed, virtualized, and scaled can be guided using real time context aware decision making. We are investigating how to apply Adaptive Policy to the problem of optimizing interactive video streaming delivery in a virtualized environment. We utilise components of our previously established test bed framework and implement a single layer neural network through Adaptive Policy, in which weights assigned to network metrics are continuously adjusted through supervised test cycles, resulting in weights in proportion to their associated impact on our video stream quality. We present the initial test results from our Perceptron inspired policy-based approach to video quality optimisation through weighted network resource evaluation.