A Deep Learning Based System for Traffic Engineering in Software Defined Networks
DOI:
https://doi.org/10.18201/ijisae.2020466314Keywords:
Deep Learning, Quality of Service, Software Defined Networks, Traffic Classification, Traffic Engineering, Traffic ShapingAbstract
Traffic engineering is essential for network management, particularly in today's large networks carrying massive amounts of data. Traffic engineering aims to increase the network's efficiency and reliability through intelligent allocation of resources. In this paper, we propose a deep learning-based traffic engineering system in software-defined networks (SDN) to improve bandwidth allocation among various applications. The proposed system conducts traffic classification based on deep neural network and 1D – convolution neural network models. It aims to improve the Quality of Service (QoS) by identifying flows from various applications and distributing the identified flow to multiple queues where each queue has a different priority. Then, it applies traffic shaping in order to manage network bandwidth and the volume of incoming traffic. To increase the network's performance and avoid traffic congestion, we implement a technique that considers the port capacity to accomplish general load balancing. We have evaluated and compared the performance of deep learning and machine learning models, and tried to solve an imbalanced dataset by implementing the SMOTE technique. The experimental results show that deep models can identify traffic flows with higher accuracy than machine learning models, and applying traffic shaping to the identified flow increases the network's performance and bandwidth availability.
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