Virtual network function deployment algorithm based on graph convolution deep reinforcement learning.

In Network Function virtualization (NFV), network functions are virtualized as Virtual Network functions (VNFs). A network service consists of a set of VNFs. One of the major challenges in implementing this paradigm is allocating optimal resources to VNFs. Most existing work assumes that services are represented as service functional chains (SFC), which are chains. However, for more complex and diversified network services, a more appropriate representation is Virtual Network Function Forwarding Graph (VNF-FG), namely directed acyclic Graph (DAGs). Previous works failed to take advantage of this special graph structure, which makes them unsuitable for complex and diverse network service scenarios. Aiming at the problem of virtual network function Forwarding Graph mapping (VNF-FGE) represented by DAG, this paper proposes a virtual network function (VNF) deployment algorithm based on graph convolution network (GCN) and deep reinforcement learning (DRL), called GDRL-VNFP. First, we describe the VNF-FGE problem as an integer linear programming (ILP) problem and the virtual network service request (NSR) as a DAG. Secondly, in order to overcome the challenges brought about by the different sizes and dynamic arrival of the DAG representation of NSR, an efficient algorithm based on GCN and DRL is proposed, which can generate deployment solutions in real time under the premise of meeting the quality of service and minimize the total cost of deployment. We use GCN to extract features from the physical network topology and VNF-FG represented by DAG, and in addition, and construct a sequence-to-sequence model based on the attention mechanism for the output mapping scheme. Finally, we trained the model using a policy-based gradient-based reinforcement learning algorithm that could quickly find a near-optimal solution for the problem instance. Simulation results show that our GDRL-VNFP outperforms state-of-the-art solutions in terms of deployment cost, service request reception rate and runtime. [ABSTRACT FROM AUTHOR]