Applying Big Data, Machine Learning, and SDN/NFV for 5G Early-Stage Traffic Classification and Network QoS Control

Luong Vy Le, Bao-Shuh Lin, Do Sinh

Research output: Contribution to journalArticlepeer-review

Abstract

Due to the rapid growth of mobile broadband and IoT applications, the early-stage mobile traffic classification becomes more important for traffic engineering to guarantee Quality of Service (QoS), implement resource management, and network security. Therefore, identifying traffic flows based on a few packets during the early state has attracted attention in both academic and industrial fields. However, a powerful and flexible platform to handle millions of traffic flows is still challenging. This study aims to demonstrate how to integrate various state-of-the-art machine learning (ML) algorithms, big data analytics platforms, software-defined networking (SDN), and network functions virtualization (NFV) to build a comprehensive framework for developing future 5G SON applications. This platform successfully collected, stored, analyzed, and identified a huge number of real-time traffic flows at broadband Mobile Lab (BML), National Chiao Tung University (NCTU). Moreover, we also implemented network QoS control to configure priorities per-flow traffic to enable bandwidth guarantees for each application by using SDN. Finally, the performance of the proposed models was evaluated by applying them to a real testbed environment. The powerful computing capacity of the platform was also analyzed.
Original languageEnglish
Pages (from-to)36-50
Number of pages15
JournalTransactions on Networks and Communications
Volume6
Issue number2
DOIs
StatePublished - 3 May 2018

Keywords

  • Traffic classification; Machine Learning; Big Data; SON; 5G; InfoSphere; Streaming

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