Meta Relational Learning-Based Service-Tailored VNF Deployment for B5G Network Slice
Zexi Xu, Lei Zhuang, Weihua Zhuang, Yuxiang Hu, Wenshuai Mo, Zihao Wang · IEEE Internet of Things Journal · 2023
To bring 5G systems and networks to life in large-scale commercial applications, academia community has started the research beyond 5G (B5G), in which network slicing (NS) is proposed as a new paradigm for building service-tailored B5G networks. In each network slice, to precisely control the service quality and cost, deploying the service-required virtual network functions (VNFs) by utilizing the linkage between the characteristics of this slicing task and the characteristics of different servers in the B5G network is essential. Therefore, aiming at gaining the ability of learning and adapting new tasks quickly and cost effectively, we view the NFV deployment problem as a meta relational learning process that explores the meta mapping relation between service-tailored slicing tasks and the B5G physical network and propose a service-tailored VNF deployment framework, abbreviated as StailNet. Instead of training a one-strategy-fits-all deployment model, we focus on “learning” how to train a deployment model and propose to learn the features of servers and slicing tasks from the perspective of knowledge graph-based representation learning, then locate the initial meta mapping relation by extracting meta information in the task-agnostic meta space and exploring the service-tailored meta mapping relation in the task space for each task, so that we can quickly obtain the solution by a few gradients on the initial meta mapping relation. To highlight the performances of StailNet, we do comprehensive simulations. Simulation results demonstrate that our StailNet outperforms the selected representative algorithms in the literature.