Novel Approaches for VNF Requirement Prediction Using DNN and LSTM

Network Function Virtualization (NFV) is gaining popularity among network operators to provide cost effective and dynamic network services. NFV enables faster service by deploying virtual instances of network functions. While serving dynamic and varying traffic demands, network operators can get benefit from knowing the requirement for the number of Virtual Network Functions (VNFs), ahead of time. VNF requirement prediction method mostly depends on the fluctuation of network traffic load. Predicting the required number of VNFs helps the operator to manage network resources in better ways. VNF prediction method is being considered as an interesting research field for researchers. In our research, we propose VNF requirement prediction methods based on Deep Neural Networks (DNN) and Long Short Term Memory (LSTM) Networks. We provide experimental results which show promising accuracy and improvement of our method, compared to machine learning approaches used before for VNF requirement prediction. Network resource management techniques can be benefited enormously from our higher accuracy based approaches.