基于深度学习的智能应用感知网络资源分配VNF

Jun Xu, Jingyu Wang, Q. Qi, Haifeng Sun, Bo He
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引用次数: 6

摘要

应用感知是流量工程和服务质量(QoS)保障的关键,特别是在物联网(IoT)中。集中控制网络资源的软件定义网络(SDN)为细粒度的资源分配提供了机会。但是,由于流量数据的采样和识别需要消耗大量的IO资源和计算资源,控制器无法有效地自主识别应用。在这个演示中,我们提供了一个智能的应用感知虚拟化网络功能(VNF),利用深度学习技术来识别网络流量。将流量类型信息映射到特定的网络需求,并根据不同的应用程序搜索合适的路由路径。智能VNF部署在具有gpu的独立服务器上,工作在SDN的数据平面。它通过OpenFlow协议对流量进行识别,并将类型信息发送给控制器。实验表明,通过引入类型信息,SDN控制器可以为不同类型的流量分配更合适的路由路径,极大地提高了网络的QoS。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
IARA: An Intelligent Application-Aware VNF for Network Resource Allocation with Deep Learning
Application awareness is essential for traffic engineering and Quality of Service (QoS) guarantee, especially in Internet of Things (IoT). Software Defined Network (SDN) with centralized controlling of network resources provides opportunities for fine- grained resource allocation. However, the controller cannot autonomously identify applications effectively, because sampling and recognizing traffic data consumes a lot of IO and computing resources. In this demonstration, we provide an intelligent application-aware Virtualized Network Function (VNF) with deep learning technology to identify the network traffic. The traffic type information will be mapped to specific network requirements and utilized to search appropriate route paths for different applications. The intelligent VNF is deployed on a GPU-equipped standalone server and works on the data plane of SDN. It identifies the traffic and sends the type information to the controller through OpenFlow protocol. The experiments show that by introducing the type information, SDN controller can assign more appropriate route paths for different types of traffic and highly improve the network QoS.
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