Federated Traffic Engineering with Supervised Learning in Multi-region Networks

Minghao Ye, Junjie Zhang, Zehua Guo, H. J. Chao
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引用次数: 9

Abstract

Network operators usually adopt Traffic Engineering (TE) to configure the routing in their networks to achieve good load balancing performance and high resource utilization. While centralized TE can effectively improve network performance with a global view of the network, distributed TE has been considered as an alternative to manage large-scale networks that are usually partitioned into multiple regions. However, it is challenging for distributed TE to reach a global optimal performance since each region can make its local routing decisions only based on partially observed network states. In this paper, we propose a novel distributed TE scheme called FedTe, which leverages supervised learning coupled with a collaborative approach to improve the overall load balancing performance for multi-region networks. FedTe learns from the global optimal routing strategy in a centralized offline manner and predicts the optimal distribution of cross-region traffic among different regions through distributed deployment in real time. The predicted cross-region traffic distribution is integrated with measured local traffic to construct each region’s optimal regional traffic matrix, which is used to perform intra-region TE optimization. FedTe can also handle dynamic traffic variation and link failures with a 2-layer hierarchical graph neural network architecture. To validate the effectiveness of the proposed scheme, we evaluate FedTe with two real-world network topologies and a large-scale synthetic topology. Extensive evaluation results show that FedTe can achieve near-optimal load balancing performance and outperform state-of-the-art distributed TE approaches by up to 28.9% on average.
多区域网络中具有监督学习的联邦流量工程
网络运营商通常采用TE (Traffic Engineering)技术对网络中的路由进行配置,以达到良好的负载均衡性能和较高的资源利用率。虽然集中式TE可以通过网络的全局视图有效地提高网络性能,但分布式TE已被认为是管理通常划分为多个区域的大规模网络的替代方案。然而,由于每个区域只能根据部分观察到的网络状态做出局部路由决策,因此分布式TE要达到全局最优性能是具有挑战性的。在本文中,我们提出了一种新的分布式TE方案,称为FedTe,它利用监督学习和协作方法来提高多区域网络的整体负载平衡性能。FedTe以集中式离线方式学习全局最优路由策略,并通过分布式部署实时预测跨区域流量在不同区域之间的最优分布。将预测的跨区域流量分布与本地实测流量相结合,构建各区域的最优区域流量矩阵,用于区域内TE优化。FedTe还可以处理动态流量变化和链路故障的2层分层图神经网络体系结构。为了验证所提出方案的有效性,我们使用两个真实网络拓扑和一个大规模合成拓扑来评估FedTe。广泛的评估结果表明,FedTe可以实现近乎最佳的负载平衡性能,并且比最先进的分布式TE方法平均高出28.9%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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