Virtualized Network Function Forwarding Graph Placing in SDN and NFV-Enabled IoT Networks: A Graph Neural Network Assisted Deep Reinforcement Learning Method

Abstract

With an ambitious increase in the number of Internet of Things (IoT) terminals, IoT networks face a huge challenge which is providing diverse and complex network services with different requirements on a common infrastructure. To solve this challenge, Software Defined Network (SDN) and Network Function Virtualization (NFV) are adopted to build next-generation IoT networks which are softwarized and virtualized. This way, network functions are virtualized as Virtualized Network Functions (VNFs) and a network service consists of a set of VNFs. One of the main challenges for realizing this paradigm is the optimal resource allocation for VNFs. Most existing works assumed that services are represented as Service Function Chains (SFCs) which are chains. However, network services in IoT networks are more complex and diverse, therefore, more appropriate representations are Virtualized Network Function Forwarding Graphs (VNF-FGs) which are Directed Acyclic Graphs (DAGs). Previous works failed to exploit this special graph structure, which makes them sub-optimal or non-applicable for IoT networks. In this paper, we investigate the VNF-FG placing problem in dynamic IoT networks where DAG-represented services arrive and depart. To fully exploit the graph structures of services and handle the complexity of dynamic IoT networks, we combine a novel neural network structure Graph Neural Network (GNN) with Deep Reinforcement Learning (DRL) and propose an efficient algorithm for VNF-FG placing, which is called Kolin. Extensive simulation results suggest that Kolin outperforms the state-of-the-art solutions in terms of system cost, acceptance ratio, and computation complexity.