A Dynamic and Collaborative Multi-Layer Virtual Network Embedding Algorithm in SDN Based on Reinforcement Learning
Meilian Lu, Yun Gu, Dongliang Xie
Abstract
Most of existing virtual network embedding (VNE) algorithms only consider how to construct virtual networks more efficiently on a physical infrastructure, without considering the possibility that the constructed virtual networks may be further virtualized to multiple smaller ones. We define the former scenario as single-layer VNE and the later as multi-layer VNE. As the increasing popularity of deploying large datacenter networks and wide area networks with Software Defined Network (SDN) architectures, it becomes a new requirement and possibility to provide multi-layer encapsulated network services for large tenants who have hierarchical organizational structures or need fine-grained service isolation. However, existing VNE algorithm are not specifically designed for the above requirement and not flexible enough to deal with mapping virtual network requirements (VNRs) to a physical network and smaller VNRs to a mapped virtual network. In this paper, we aim to propose a unified and flexible multi-layer VNE algorithm combining with reinforcement learning to solve the embedding of multi-layer VNRs, which can better distinguish the differences between VNRs and physical networks. Simulation results show that our algorithm achieves good performance both in single-layer and multi-layer VNE scenarios.