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.

中文翻译：

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基于强化学习的SDN动态协同多层虚拟网络嵌入算法

大多数现有的虚拟网络嵌入（VNE）算法只考虑如何在物理基础设施上更有效地构建虚拟网络，而没有考虑构建的虚拟网络可能进一步虚拟化为多个较小的网络的可能性。我们将前一种场景定义为单层 VNE，将后一种场景定义为多层 VNE。随着部署具有软件定义网络（SDN）架构的大型数据中心网络和广域网的日益普及，为具有分层组织结构或需要细粒度的大型租户提供多层封装的网络服务成为新的需求和可能性。服务隔离。然而，现有的 VNE 算法不是专门为上述要求设计的，并且不够灵活，无法处理将虚拟网络要求 (VNR) 映射到物理网络以及将较小的 VNR 映射到映射虚拟网络。在本文中，我们旨在提出一种统一灵活的多层 VNE 算法，结合强化学习来解决多层 VNR 的嵌入问题，可以更好地区分 VNR 和物理网络之间的差异。仿真结果表明，我们的算法在单层和多层 VNE 场景中都取得了良好的性能。可以更好地区分VNR和物理网络的区别。仿真结果表明，我们的算法在单层和多层 VNE 场景中都取得了良好的性能。可以更好地区分VNR和物理网络的区别。仿真结果表明，我们的算法在单层和多层 VNE 场景中都取得了良好的性能。