零负载:基于零接触网络的路由器管理方案,底层6G-IoT生态系统

Pronaya Bhattacharya, Anwesha Mukherjee, S. Tanwar, Emil Pricop
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引用次数: 0

摘要

随着数据量的不断增长,6G-IoT (6G-IoT)网络管理功能面临严重瓶颈,限制了设备、协议和终端应用之间的控制、灵活性和互操作性。软件定义网络(SDN)和网络功能虚拟化(NFV)等解决方案在6G中被提出,但核心管理操作仍然是手动的。因此,为了在降低成本编排复杂性的情况下自动升级这些6G-IoT网络,提出了零接触网络(ZTN)。6G-IoT中的ZTN可实现高度自动化和业务无缝集成。本文提出了一种将ZTN集成在6G-IoT应用核心路由功能中的方案Zero-Load。提出了一种基于ZTN网络栈的核心路由器负载均衡和流量分类方案。ZTN路由器配置结构将应用程序与核心服务连接起来。进一步,在ZTN自动化层提出了基于高斯核的支持向量机分类器,对正常流量和攻击流量进行分类。将提议的工作与基准SDN和NFV方案的平均响应时间(MTTR)和分辨率延迟等参数进行比较。使用ZTN,在MTTR中获得了32.45%的平均改进,在分辨率延迟(针对查询)方面获得了87.89%的平均改进。使用高斯RBF核,精度为0.9914。这些结果表明,基于ztn的管理为更密集、更智能的6G-IoT网络铺平了道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Zero-Load: A Zero Touch Network based router management scheme underlying 6G-IoT ecosystems
The rising data volumes force significant bottlenecks on the 6G for IoT (6G-IoT) network management functions, which limits the control, flexibility, and interoperability among devices, protocols, and end applications. Solutions like software-defined networking (SDN), and network function virtualization (NFV) are proposed with 6G, but the core management operations are still manual. Thus, to automatically upscale these 6G-IoT networks at reduced cost orchestration complexity, zero-touch networks (ZTN) are proposed. ZTN in 6G-IoT allows a high degree of automation and seamless integration of services. The article proposes a scheme, Zero-Load, that integrates ZTN at the core routing functionality of the 6G-IoT applications. We present a load balancing and traffic classification scheme through the ZTN networking stack for core routers. The ZTN router configuration fabric connects applications with the core services. Further, we present a Gaussian kernel-based support vector machine (SVM) classifier at the ZTN automation layer, which classifies the normal traffic and attack traffic. The proposed work is compared for parameters like mean time to response (MTTR), and resolution latency against baseline SDN and NFV schemes. Using ZTN, an average improvement of 32.45% is obtained in MTTR, and 87.89% in resolution latency (against a query). Using the Gaussian RBF kernel, an accuracy of 0.9914 is reported. These results indicate that ZTN-based management paves the way toward a more dense and intelligent 6G-IoT network.
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