Machine Learning-based Prediction of VNF Deployment Decisions in Dynamic Networks

In addition to providing network operators with benefits in terms of flexibility and cost efficiency, softwarization paradigms like SDN and NFV are key enablers for the concept of Service Function Chaining (SFC). The corresponding networks need to support a wide range of services and applications with highly dynamic temporal profiles and heterogeneous demands. Hence, efficient management and operation of such networks requires a high degree of automation that is paired with fast and proactive decisions in order to cope with these phenomena. In particular, determining the optimal number of VNF instances that is required for accommodating current and upcoming demands is a crucial task that also affects subsequent management decisions. To enable fast and proactive decisions in this context, we propose a machine learning-based approach that uses recent monitoring data to predict whether to adapt the current number of VNF instances of a given type. Furthermore, we present a work flow for generating labeled training data that reflects temporal dynamics and heterogeneous demands of real world networks. In addition to demonstrating the feasibility of the approach in a case study, we provide guidelines regarding the choice of monitoring data that should be collected for reliable prediction as well as the amount of data that is required to train such a predictor.