Towards Efficient and Scalable Machine Learning-Based QoS Traffic Classification in Software-Defined Network

Audah, M. Z. Fatimah; Chin, Tan Saw; Zulfadzli, Y.; Lee, C. K.; Rizaluddin, K.

doi:10.1007/978-3-030-27192-3_17

Cited by 12 publications

(6 citation statements)

References 24 publications

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“…As a result, several source applications can be recognized. The approach can classify flows into clusters with similar patterns by detecting trends in their properties such as the size of the initial few packets, arrival timings, packet length, IP address, round trip time, and source/destination ports [55,56].…”

Section: Statistical-based Tcmentioning

confidence: 99%

Machine-Learning-Based Traffic Classification in Software-Defined Networks

Serag,

Abdalzaher,

Elsayed

et al. 2024

Electronics

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Many research efforts have gone into upgrading antiquated communication network infrastructures with better ones to support contemporary services and applications. Smart networks can adapt to new technologies and traffic trends on their own. Software-defined networking (SDN) separates the control plane from the data plane and runs programs in one place, changing network management. New technologies like SDN and machine learning (ML) could improve network performance and QoS. This paper presents a comprehensive research study on integrating SDN with ML to improve network performance and quality-of-service (QoS). The study primarily investigates ML classification methods, highlighting their significance in the context of traffic classification (TC). Additionally, traditional methods are discussed to clarify the ML outperformance observed throughout our investigation, underscoring the superiority of ML algorithms in SDN TC. The study describes how labeled traffic data can be used to train ML models for appropriately classifying SDN TC flows. It examines the pros and downsides of dynamic and adaptive TC using ML algorithms. The research also examines how ML may improve SDN security. It explores using ML for anomaly detection, intrusion detection, and attack mitigation in SDN networks, stressing the proactive threat-detection and response benefits. Finally, we discuss the SDN-ML QoS integration problems and research gaps. Furthermore, scalability and performance issues in large-scale SDN implementations are identified as potential issues and areas for additional research.

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Section: Statistical-based Tcmentioning

confidence: 99%

Machine-Learning-Based Traffic Classification in Software-Defined Networks

Serag,

Abdalzaher,

Elsayed

et al. 2024

Electronics

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“…Providing ubiquitous connectivity for various devices with different QoS requirements is one of the most challenging issues for mobile network operators [13]. This problem is amplified in future 5G applications with strict QoS requirements [14].…”

Section: Related Workmentioning

confidence: 99%

Actor-Critic Learning Based QoS-Aware Scheduler for Reconfigurable Wireless Networks

Mollahasani¹,

Erol‐Kantarci²,

Hirab

et al. 2022

IEEE Trans. Netw. Sci. Eng.

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The flexibility offered by reconfigurable wireless networks, provide new opportunities for various applications such as online AR/VR gaming, high-quality video streaming and autonomous vehicles, that desire high-bandwidth, reliable and low-latency communications. These applications come with very stringent Quality of Service (QoS) requirements and increase the burden over mobile networks. Currently, there is a huge spectrum scarcity due to the massive data explosion and this problem can be solved by helps of Reconfigurable Wireless Networks (RWNs) where nodes have reconfiguration and perception capabilities. Therefore, a necessity of AI-assisted algorithms for resource block allocation is observed. To tackle this challenge, in this paper, we propose an actor-critic learning-based scheduler for allocating resource blocks in a RWN. Various traffic types with different QoS levels are assigned to our agents to provide more realistic results. We also include mobility in our simulations to increase the dynamicity of networks. The proposed model is compared with another actor-critic model and with other traditional schedulers; proportional fair (PF) and Channel and QoS Aware (CQA) techniques. The proposed models are evaluated by considering the delay experienced by user equipment (UEs), successful transmissions and head-of-the-line delays. The results show that the proposed model noticeably outperforms other techniques in different aspects.

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“…However, most existing models lack the consideration of model efficiency, where they neither take into account the time and space complexity nor fully evaluate the efficiency. In particular, for deep learning methods, the huge overhead of memory and runtime in the complex neural network leads to high energy consumption, which is not feasible for edge devices with limited resources [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Lightweight Traffic Classification Model Based on Deep Learning

Sun

Chen

et al. 2022

Wireless Communications and Mobile Computing

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The development of mobile computing and the Internet of Things (IoT) has led to a surge in traffic volume, which creates a heavy burden for efficient network management. The network management requires high computational overheads to make traffic classification, which is even worse when in edge networks; existing approaches sacrifice the efficiency to obtain high-precision classification results, which are no longer suitable for limited resources edge network scenario. Given the problem, existing traffic classification generally has huge parameters and especially computational complexity. We propose a lightweight traffic classification model based on the Mobilenetv3 and improve it for an ingenious balance between performance and lightweight. Firstly, we adjust the model scale, width, and resolution to substantially reduce the number of model parameters and computations. Secondly, we embed precise spatial information on the attention mechanism to enhance the traffic flow-level feature extraction capability. Thirdly, we use the lightweight multiscale feature fusion to obtain the multiscale flow-level features of traffic. Experiments show that our model has excellent classification accuracy and operational efficiency. The accuracy of the traffic classification model designed in our work has reached more than 99.82%, and the parameter and computation amount are significantly reduced to 0.26 M and 5.26 M. In addition, the simulation experiments on Raspberry Pi prove the proposed model can realize real-time classification capability in the edge network.

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Towards Efficient and Scalable Machine Learning-Based QoS Traffic Classification in Software-Defined Network

Cited by 12 publications

References 24 publications

Machine-Learning-Based Traffic Classification in Software-Defined Networks

Machine-Learning-Based Traffic Classification in Software-Defined Networks

Actor-Critic Learning Based QoS-Aware Scheduler for Reconfigurable Wireless Networks

Lightweight Traffic Classification Model Based on Deep Learning

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