An Approach Based on Knowledge-Defined Networking for Identifying Video Streaming Flows in 5G Networks

Abstract

5G aims to provide a complete wireless communication system with various applications, network services and technologies. In terms of 5G network management, Software-Defined Networking (SDN), and Network Functions Virtualization (NFV)are expected to control and manage network resources. Network Softwarization provides better management of network traffic. However, it does not guarantee network performance will not degradation when the traffic rises. Flow identification has been raised as a solution for keeping the network performance, and it has become a hot topic in both, academy and industry. In particular, there is a high interest in identifying video streaming flows since thanks to 5G and its benefits that improve the streaming media industry, the video streaming traffic is expected to increase dramatically due to the massive connection of 5G compatible devices. Motivated by this, we presented a novel approach for identifying video streaming services. Our approach includes three modules: video stream acquisition module, video stream analyzer module, and application module. In the video stream acquisition module, we capture video streaming packets and organize them in to flow records. In the video streaming analyzer module, we analyze the flow records using supervised machine learning algorithms to find the appropriate algorithm that performs better. In the application module, we provide a brief explanation of the applications of our approach. Additionally, we provide an analysis of the overall workload generated by our approach. The results of the evaluation by module corroborate the usefulness and feasibility of our approach for identifying video streaming services.