Deep Customized Network Slicing and Efficient Routing for IoT Applications in B5G-Enabled Edge Computing Networks

Beyond 5G-enabled edge computing networking (ECN) will further deploy computing and communication resources to the edge of the networks. Then, edge service demands for Internet of Things (IoT) applications are becoming more and more diverse, while the corresponding routing service capability is limited and not flexible enough to deal with the demands of ECN, which then leads to reducing the inherent routing capability of ECN. It becomes extremely difficult for ECN to support diversified demands and provide diverse IoT applications quickly and flexibly. In this article, we propose a novel and customized deep routing mechanism for IoT applications in ECN, in which the network slicing and deep learning methods are jointly applied and leveraged. First, we design a new ECN architecture that formulates four kinds of network slices to cope with various IoT scenarios, which are eMBB, uRLLC, mMTTC, and backup slices. Second, using these slices, we can customize the ECN environment flexibly, based on which we propose the corresponding routing method for the purpose of fast and efficient service delivery. In particular, the mapping between network slices and the infrastructure is established with the object of maximizing the resource utilization. Then, the routing is designed and customized by using the deep learning model. Lastly, the experimental results show that the deep customized mechanism designed in this article can reduce the average loss rate of the model, decrease the average delay, as well as improve the average resource utilization compared with the existing studies.