Data Traffic Classification in Software Defined Networks (SDN) using supervised-learning

Raikar, Meenaxi M.; Meena, S M; Mulla, Mohammed Moin; Shetti, Nagashree S.; Karanandi, Meghana

doi:10.1016/j.procs.2020.04.299

Cited by 63 publications

(30 citation statements)

References 15 publications

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“…Raikar et al [91] proposed a traffic classification using supervised learning models in the SDN environment. Specifically, a brief comparative analysis of SVM, Naïve Bayes, and KNN has been done, where the accuracy of traffic classification is greater than 90% in all the three supervised learning models.…”

Section: Fine-grained Traffic Classificationmentioning

confidence: 99%

Intelligent Traffic Management in Next-Generation Networks

2022

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The recent development of smart devices has lead to an explosion in data generation and heterogeneity. Hence, current networks should evolve to become more intelligent, efficient, and most importantly, scalable in order to deal with the evolution of network traffic. In recent years, network softwarization has drawn significant attention from both industry and academia, as it is essential for the flexible control of networks. At the same time, machine learning (ML) and especially deep learning (DL) methods have also been deployed to solve complex problems without explicit programming. These methods can model and learn network traffic behavior using training data/environments. The research community has advocated the application of ML/DL in softwarized environments for network traffic management, including traffic classification, prediction, and anomaly detection. In this paper, we survey the state of the art on these topics. We start by presenting a comprehensive background beginning from conventional ML algorithms and DL and follow this with a focus on different dimensionality reduction techniques. Afterward, we present the study of ML/DL applications in sofwarized environments. Finally, we highlight the issues and challenges that should be considered.

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Section: Fine-grained Traffic Classificationmentioning

confidence: 99%

Intelligent Traffic Management in Next-Generation Networks

2022

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“…They design the flow table representation and extraction procedure. The combination of machine learning methods and SDN is presented in [31]. Some machine learning methods are used for traffic classification based on the applications in a network with SDN architecture.…”

Section: ) Network Componentmentioning

confidence: 99%

“…The common point seen in most previous research studies is that they all point to solving the real time problem, but they try to speed up tasks and not meet the deadline. In [30] and [31], practical methods for classifying data streams are presented, but their analytical techniques are time-consuming, which makes the application of these methods impossible for timely applications such as telesurgery. Instead, our proposed method for flow separation is straightforward and, of course, fast, which makes it ideal for use in telesurgery applications with strict real time constraints.…”

Section: ) Network Componentmentioning

confidence: 99%

RT-TelSurg: Real Time Telesurgery Using SDN, Fog, and Cloud as Infrastructures

Sedaghat

Jahangir

2021

IEEE Access

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This paper proposes a novel and efficient real time network architecture, named RT-TelSurg, for one of the most appealing tactile Internet applications, i.e., Telesurgery. In telesurgery, the patient's vital signs and status and the required robotic commands during the surgery should be received on time. Otherwise, the life of the patient or the safety of the operation is endangered. Hence, transmitted packets should meet their respective relative deadlines. Software-defined networking is a relatively new architecture for computer and telecommunications networks in which the network control plane is separated from the data plane. One way to achieve real time telesurgery is to employ cloud and fog networks using SDN as infrastructure. By using a real time cloud controller in the SDN as the core and a fog controller on the edge of the network on the master (physician) side, one may satisfy the acceptable level of the timing constraints of the telesurgery. Accordingly, based on the presented architecture, we develop the statistical performance model of our proposed approach, RT-TelSurg. This statistical queuing theory model is designed for critical and non-critical states of the network. Based on these states, network resources are allocated to telesurgery data in both modes, and a computational bottleneck is detected. RT-TelSurg is evaluated according to real time and surgery efficiency parameters. The results show that the average deadline hit ratio is 98.2% in different conditions, which is quite acceptable for telesurgery applications.

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“…The host h2 is the publisher, and the host h4 is the subscriber. The "mosquitto" broker is used for the demonstration of the publish-subscribe model for IoT traffic [18]. The supervised learning model is used for classifying the video and IoT traffic.…”

Section: Simulation Environment Setupmentioning

confidence: 99%

Network Slicing in Software-Defined Networks for Resource Optimization

Raikar

Meena

2021

Intelligent Sustainable Systems

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The Internet users' heterogeneous requirements pose a challenge to the service provider for catering to the network infrastructure needs to provide the different services. The partitioning of the shared physical network is proposed using a network slicing mechanism to meet this challenge. The classification of the services is carried out based on the attributes in the flow features. The flow features are extracted in real-time for traffic classification. The traffic classification is implemented using supervised learning models for different network services. The combination of traffic classification and network slicing is proposed for the dynamic allocation of resources. In this paper, the testbed to demonstrate the network slicing concept based on the bandwidth requirement is presented using FlowVisor and the OpenFlow protocol in software-defined networks. The intelligence is embedded into networks by integrating decision making using a supervised learning model for classification followed by network slicing for optimal resource allocation.

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Data Traffic Classification in Software Defined Networks (SDN) using supervised-learning

Cited by 63 publications

References 15 publications

Intelligent Traffic Management in Next-Generation Networks

Intelligent Traffic Management in Next-Generation Networks

RT-TelSurg: Real Time Telesurgery Using SDN, Fog, and Cloud as Infrastructures

Network Slicing in Software-Defined Networks for Resource Optimization

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