A Parallel Placement Approach for Service Function Chain Using Deep Reinforcement Learning

Abstract

Network Function Virtualization (NFV) enables service flexibility and cost reduction by replacing traditional hardware middle-boxes with Virtual Network Functions (VNFs) running on general-purpose servers. Normally, network traffic usually needs to pass through several VNFs in a particular order. This phenomenon is known as Service Function Chaining (SFC). How to place SFCs with minimal resource is still an open problem. Most of the existing work thinks it's difficult to find the placement solution for all SFCs as a whole, they, instead, consider each demand sequentially, and deploy SFCs one by one. But such serial placement lacks consideration of the interrelations among demands and unable to minimize resource. In this paper, we innovatively propose a parallel deployment scheme based on Deep Reinforcement Learning (DRL). It satisfies demands with minimum resource. We design an overall SFC placement scheme for all demands, and deploy all SFCs simultaneously. We evaluate the proposed algorithms using extensive simulations and prototype experiments. The result demonstrates that our parallel deployment approach minimized resource costs compared with other schemes.