Performance Evaluation of Supervised ML Algorithms for Elephant Flow Detection in SDN

Kaoutar Boussaoud, Meryeme Ayache, A. En-Nouaary
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Abstract

Software-defined networking (SDN) improves the network management due to the separation of the network control plane from the packet forwarding plane. However, with the increase in data traffic, SDN architectures have raised several challenges in terms of traffic engineering, QoS, and network management. Therefore, it is crucial to develop an intelligent system to classify the flows and predict future traffic. Indeed, in order to propose an adequate forwarding strategy for various flow types (particularly elephant flows (EFs)) in an SDN environment, an accurate flow detection system is required. Hence, in this paper, we propose a model-based SDN controller that includes machine learning algorithms to detect large-size traffic and forward it. Moreover, we represent a comparative simulation to evaluate the performance of some supervised machine learning algorithms such as Naive Bayes (NB), K-Nearest neighbors (KNN), Logistics regression (RL), Support Vector Machine (SVM), and Decision Tree (DT), to detect the elephant flow. A decision tree (DT) and K-Nearest neighbors (KNN) are the best candidate machine learning algorithms in elephant flow detection with an accuracy of 99%.
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SDN中大象流检测的监督ML算法性能评价
SDN (Software-defined networking)通过网络控制平面和报文转发平面的分离,改善了网络管理。然而,随着数据流量的增加,SDN架构在流量工程、QoS和网络管理方面提出了一些挑战。因此,开发一种智能系统来进行流量分类和预测未来的交通是至关重要的。实际上,为了在SDN环境中针对各种流量类型(特别是象流)提出适当的转发策略,需要一个精确的流量检测系统。因此,在本文中,我们提出了一种基于模型的SDN控制器,其中包括机器学习算法来检测大流量并转发它。此外,我们还代表了一个比较模拟来评估一些监督机器学习算法的性能,如朴素贝叶斯(NB)、k近邻(KNN)、物流回归(RL)、支持向量机(SVM)和决策树(DT),以检测大象流。决策树(DT)和k近邻(KNN)是大象流检测中最好的候选机器学习算法,准确率为99%。
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