Ensemble Deep Learning Aided VNF Deployment for IoT Services

In the sixth generation (6G) networks, due to the massive Internet of Things (IoT) connectivity and substantial growth of communication traffic, an effective Virtual Network Function (VNF) orchestration scheme is anticipated to function dynamically and intelligently. Moving beyond the traditional paradigm of the VNF orchestration and employing VNFs on the network edge located cloudlets based on the inspiration from multi-access edge computing can intensify the overall performance of delay-sensitive applications. In this paper, we intend to investigate how to simultaneously leverage the ensembling of multiple deep learning models for proper calibration to provide real-time VNF placement solutions. We also address the challenges associated with state-of-the-art approaches to deal with dynamic network traffic and topology patterns. Our envisioned methods, based on Convolutional Neural Networks and Artificial Neural Networks named as E-ConvNets and E-ANN respectively, suggest two proactive VNF deployment strategies. These VNF placement strategies demonstrate (simulation results) encouraging performance (optimality gap nearly 7%) in terms of minimizing relocation and communication costs, and high scalability intelligence factor (around 0.93). Moreover, the presented results are further indications of integrating edge computing and deep learning-based strategies into similar research enigmas for future telecommunication networks.