Distributed Multi Criteria Routed for MPLS-TE Based on Machine Learning: Concept and Applications

J. Oubaha, Ali Ouacha, Hassan Echoukairi
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引用次数: 1

Abstract

MPLS-TE Networks are complex interacting systems, providers integrate absolutely the forwarding equivalent class involving in the dynamic routing algorithm "Dynamic Multi Criteria Routing", especially with network services such as an IPtv, a VPNs or a VoIP with the several types of video streaming and similar user applications, making its flagship technologies of tomorrow. Machine learning is very compatible for this kind of networks. In this paper, we first review how the main machine learning concepts can be integrated in communication networks and discuss concepts, supervised and unsupervised model suitable to the network, data and management strategies, and creating a new architecture of network controls and management tools. We then describe case studies networking in detail, anticipate anomalies at multiple network layers, covering predictive maintenance, descriptive network topology management, capacity optimization. Finally, we prove the importance of this work, and guess an overview of intelligent dynamic networks.
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基于机器学习的MPLS-TE分布式多准则路由:概念与应用
MPLS-TE网络是一个复杂的交互系统,提供商完全集成了动态路由算法“动态多标准路由”中涉及的转发等效类,特别是与IPtv, vpn或VoIP等网络服务以及几种类型的视频流和类似的用户应用程序相结合,使其成为未来的旗舰技术。机器学习与这类网络非常兼容。在本文中,我们首先回顾了如何将主要的机器学习概念集成到通信网络中,并讨论了适用于网络的监督和无监督模型、数据和管理策略的概念,以及创建网络控制和管理工具的新架构。然后,我们详细描述了网络案例研究,预测了多个网络层的异常情况,包括预测性维护、描述性网络拓扑管理和容量优化。最后,我们证明了本工作的重要性,并对智能动态网络进行了展望。
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