DeepNFV: A Lightweight Framework for Intelligent Edge Network Functions Virtualization
DeepNFV: A Lightweight Framework for Intelligent Edge Network Functions Virtualization
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DOI：
10.1109/mnet.2018.1700394
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发表时间：
2018-08
期刊：
影响因子：
9.3
通讯作者：
Liangzhi Li;K. Ota;M. Dong
中科院分区：
文献类型：
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作者：
Liangzhi Li;K. Ota;M. Dong
传统的网络功能虚拟化（NFV）实现在某种程度上过于繁重，并且没有足够的功能来执行复杂的任务。本文提出了一个轻量级的NFV框架DeepNFV，该框架基于运行在网络边缘的Docker容器，将最先进的深度学习模型与NFV容器相结合，以解决流量分类、链接分析等复杂问题，并将DeepNFV框架与现有的几个工作进行了比较，详细介绍了其结构和功能。DeepNFV最显著的优势是其轻量级设计，这是由容器技术的虚拟化和低成本特性所导致的。此外，我们设计这个框架与边缘设备兼容，以减少中央服务器的计算开销。另一个优点是深度学习模型带来的强大分析能力，这使得它适用于比传统NFV方法更多的场景。此外，我们还描述了一些典型的应用场景，关于NFV容器如何工作以及如何利用其学习能力。仿真结果表明，它的高效率，以及在一个典型的使用情况下，出色的识别性能。
Traditional Network Functions Virtualization (NFV) implementations are somehow too heavy and do not have enough functionality to conduct complex tasks. In this work, we propose a lightweight NFV framework named DeepNFV, which is based on the Docker container running on the network edge, and integrates state-of-the-art deep learning models with NFV containers to address some complicated problems, such as traffic classification, link analysis, and so on. We compare the DeepNFV framework with several existing works, and detail its structures and functions. The most significant advantage of DeepNFV is its lightweight design, resulting from the virtualization and low-cost nature of the container technology. Also, we design this framework to be compatible with edge devices, in order to decrease the computational overhead of the central servers. Another merit is its strong analysis ability brought by deep learning models, which make it suitable for many more scenarios than traditional NFV approaches. In addition, we also describe some typical application scenarios, regarding how the NFV container works and how to utilize its learning ability. Simulations demonstrate its high efficiency, as well as the outstanding recognition performance in a typical use case.