An Analytical Queuing Model Based on SDN for IoT Traffic in 5G

Abstract

The latest mobile and wireless communication technology 5G will revolutionise the way we communicate and interact in the digital world. 5G is expected to have a large-scale impact on society, industries and the digital economy. The technology will unleash an ecosystem that enables Ultra-Reliable Low Latency Communication (URLLC) and massive Machine-Type Communication (mMTC), this will heavily benefit IoT devices. However, despite the lucrative advantages offered by 5G, the network infrastructure and operations will come with huge financial cost making capital expenditure (CAPEX) and operational expenditure (OPEX) an issue. With the advent of Software Defined Networking (SDN) and Network Function Virtualisation (NFV), most of the financial burden can be reduced through virtualisation of the access network infrastructure (eNodeB, gNodeB), these access networks send traffic from ubiquitous IoT devices to IP network switches. Considering the massive machine-type traffic and the need for URLLC, we need an efficient queuing model that can cater for the network packets in transit. This paper proposes an analytical Markovian queuing model based on M/M/C/ $$\infty $$ / $$\infty $$ to offer efficient and scalable traffic engineering for the massive traffic that transit via the 5G access networks to SDN architecture. The SDN controller and NFV will be used to implement the Markovian queuing model and to intelligently route the traffic efficiently that comes from the various 5G access networks to their final destination and egress point through the use of virtual switches.