论文标题
SDN \＆SDCN流量测量的调查：现有方法和研究挑战
A Survey on SDN \& SDCN Traffic Measurement: Existing Approaches and Research Challenge
论文作者
论文摘要
软件定义的网络（SDN）是下一代网络，它通过利用OpenFlow协议作为数据平面和控制平面之间的通信链接，将控制平面与转发设备的数据平面分解。但是，SDN的动作可能存在一些安全问题，即攻击者可以控制SDN控制平面。因此，交通测量是一种基本技术，可以保护SDN免受高安全性威胁，例如DDOS，重型击球手，超级播放器以及实时视频呼叫，QoS控制，高带宽要求，资源管理在SDN/软件定义的Cellular Network（SDCN）中也是不可避免的。在这种情况下，我们调查了SDN流量测量解决方案，以评估这些解决方案如何制造有安全，高效且健壮的SDN/SDCN体系结构。在本文中，已经根据网络应用程序行为对各种类型的SDN流量测量解决方案进行了分类。此外，我们发现了与SDN/SDCN的流量测量和未来研究范围相关的挑战，该挑战将指导设计和开发更先进的交通测量解决方案，以实现未来前景中可扩展，异构性，分层且广泛部署的SDN/SDCN的挑战。在详细的详细信息上，我们列出了类型的实用机器学习（ML）方法，以分析如何改善交通测量表演。我们得出的结论是，在SDN流量测量解决方案中使用ML将带来好处，以互补的方式获得有保证的SDN/SDCN网络。
Software Defined Network (SDN) is the next generation network that decouples the control plane from the data plane of forwarding devices by utilizing the OpenFlow protocol as a communication link between the data plane and the control plane. However, there are some security issues might be in actions on SDN that the attackers can take control over the SDN control plane. Thus, traffic measurement is a fundamental technique of protecting SDN against the high-security threats such as DDoS, heavy hitter, superspreader as well as live video calling, QoS control, high bandwidth requirement, resource management are also inevitable in SDN/Software Defined Cellular Network (SDCN). In such a scenario, we survey SDN traffic measurement solutions, in order to assess how these solutions can make a secured, efficient and robust SDN/SDCN architecture. In this paper, various types of SDN traffic measurement solutions have been categorized based on network applications behaviour. Furthermore, we find out the challenges related to SDN/SDCN traffic measurement and future scope of research, which will guide to design and develop more advanced traffic measurement solutions for a scalable, heterogeneous, hierarchical and widely deployed SDN/SDCN in future prospects. More in details, we list out kinds of practical machine learning (ML) approaches to analyze how we can make improvement in the traffic measurement performances. We conclude that using ML in SDN traffic measurement solutions will give benefit to get secured SDN/SDCN network in complementary ways.
