Graph neural network-based virtual network function deployment optimization,International Journal of Network Management

当前位置： X-MOL 学术 › Int. J. Netw. Manag. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Graph neural network-based virtual network function deployment optimization
International Journal of Network Management ( IF 1.5 ) Pub Date : 2021-05-12 , DOI: 10.1002/nem.2164
Hee‐Gon Kim ₁ , Suhyun Park ₁ , Stanislav Lange ₂ , Doyoung Lee ₁ , Dongnyeong Heo ₃ , Heeyoul Choi ₃ , Jae‐Hyoung Yoo ₁ , James Won‐Ki Hong ₁

Affiliation

Software-defined networking (SDN) and network function virtualization (NFV) help reduce the operating expenditure (OPEX) and capital expenditure (CAPEX) as well as increase the network flexibility and agility. However, since the network is more dynamic and heterogeneous than before, operators have problems to cope with the increased complexity of managing virtual networks and machines. This complexity is paired with strict time requirements for making management decisions; traditional mechanisms that rely on, for example, integer linear programming (ILP) models are no longer feasible. Machine learning has emerged as one of the possible solution to address network management problems to get near-optimal solutions in a short time. However, applying machine learning to network management is also not simple and has many challenges. Especially, understanding the network environment is an important problem for designing a machine learning model. In this paper, we proposed to use graph neural network (GNN) for virtual network function (VNF) management. The proposed model solves the complex VNF management problem in a short time and gets near-optimal solutions. We developed a model by taking into account various network environment conditions so that it can be applied in the actual network environment. Also, through in-depth experiments, we suggested the direction of the machine learning-based network management method.

中文翻译：

基于图神经网络的虚拟网络功能部署优化

软件定义网络（SDN）和网络功能虚拟化（NFV）有助于降低运营支出（OPEX）和资本支出（CAPEX），并提高网络的灵活性和敏捷性。然而，由于网络比以前更加动态和异构，运营商面临着管理虚拟网络和机器日益复杂的问题。这种复杂性与制定管理决策的严格时间要求相结合；例如，依赖于整数线性规划 (ILP) 模型的传统机制不再可行。机器学习已成为解决网络管理问题以在短时间内获得近乎最佳解决方案的可能解决方案之一。然而，将机器学习应用到网络管理中也并不简单，面临诸多挑战。尤其，理解网络环境是设计机器学习模型的一个重要问题。在本文中，我们建议使用图神经网络 (GNN) 进行虚拟网络功能 (VNF) 管理。所提出的模型在短时间内解决了复杂的 VNF 管理问题，并获得了接近最优的解决方案。我们综合考虑各种网络环境条件，开发了一个模型，使其能够应用于实际的网络环境中。此外，通过深入的实验，我们提出了基于机器学习的网络管理方法的方向。所提出的模型在短时间内解决了复杂的 VNF 管理问题，并获得了接近最优的解决方案。我们综合考虑各种网络环境条件，开发了一个模型，使其能够应用于实际的网络环境中。此外，通过深入的实验，我们提出了基于机器学习的网络管理方法的方向。所提出的模型在短时间内解决了复杂的 VNF 管理问题，并获得了接近最优的解决方案。我们综合考虑各种网络环境条件，开发了一个模型，使其能够应用于实际的网络环境中。此外，通过深入的实验，我们提出了基于机器学习的网络管理方法的方向。

更新日期：2021-05-12

点击分享查看原文

点击收藏

阅读更多本刊最新论文本刊介绍/投稿指南11