Intelligent Traffic Management in Next-Generation Networks

Aouedi, Ons; Piamrat, Kandaraj; Parrein, Benoît

doi:10.3390/fi14020044

Cited by 23 publications

(14 citation statements)

References 121 publications

(123 reference statements)

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“…The new direction toward logical centralization of control presented in the SDN paradigm will smoothly tame the complexity in the distributed system environment. In this context, the knowledge plane can benefit from different Machine Learning (ML) techniques that collect and utilize network knowledge to control and manage the network [46,48]. Many researchers, such as the authors of [47][48][49], support the integration of SDN and AI.…”

Section: Ai-based Solutions For Sdn Consistency Synchronizationmentioning

confidence: 99%

An Adaptive State Consistency Architecture for Distributed Software-Defined Network Controllers: An Evaluation and Design Consideration

Alsheikh,

Fadel,

Akkari

2024

Applied Sciences

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The Physically Distributed Logically Centralized (PDLC) software-defined network (SDN) control plane is physically dispersed across several controllers with a global network view for performance, scalability, and fault tolerance. This design, providing control applications with a global network view, necessitates network state synchronization among controllers. The amount of inter-controller synchronization can affect the performance and scalability of the system. The absence of standardized communication protocols for East-bound SDN interfaces underscores the need for high-performance communication among diverse SDN controllers to maintain consistent state exchange. An inconsistent controller’s network view can significantly impact network effectiveness and application performance, particularly in dynamic networks. This survey paper offers an overview of noteworthy AI and non-AI solutions for PDLC SDN architecture in industry and academia, specifically focusing on their approaches to consistency and synchronization challenges. The suggested PDLC framework achieves an adaptive controller-to-controller synchronization rate in a dynamic network environment.

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Section: Ai-based Solutions For Sdn Consistency Synchronizationmentioning

confidence: 99%

An Adaptive State Consistency Architecture for Distributed Software-Defined Network Controllers: An Evaluation and Design Consideration

Alsheikh,

Fadel,

Akkari

2024

Applied Sciences

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“…Complex systems and distributed network maintenance have also been the preoccupations of many researchers [48][49][50], and modeling of the present and future states using different models, including Markov Chains and/or Hidden Markov, are discussed in connection with some applications for several systems [51], based on the modeling of hidden states of those systems. These solutions might involve complex algorithms and also presume higher computation power for achieving usable results in the prognosis of a system's future states, as well as possible training, using simulated or collected data.…”

Section: Related Workmentioning

confidence: 99%

Smart Preventive Maintenance of Hybrid Networks and IoT Systems Using Software Sensing and Future State Prediction

Minea

Minea²,

Semenescu

2023

Sensors

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At present, IoT and intelligent applications are developed on a large scale. However, these types of new applications require stable wireless connectivity with sensors, based on several standards of communication, such as ZigBee, LoRA, nRF, Bluetooth, or cellular (LTE, 5G, etc.). The continuous expansion of these networks and services also comes with the requirement of a stable level of service, which makes the task of maintenance operators more difficult. Therefore, in this research, an integrated solution for the management of preventive maintenance is proposed, employing software-defined sensing for hardware components, applications, and client satisfaction. A specific algorithm for monitoring the levels of services was developed, and an integrated instrument to assist the management of preventive maintenance was proposed, which are based on the network of future states prediction. A case study was also investigated for smart city applications to verify the expandability and flexibility of the approach. The purpose of this research is to improve the efficiency and response time of the preventive maintenance, helping to rapidly recover the required levels of service, thus increasing the resilience of complex systems.

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“…The experimental results indicate that their approach is more effective than the conventional tree-based classifiers and other ensemble learning tree methods. For more details about the ML-based model for traffic classification, please refer to our survey [6] [1].…”

Section: A Ensemble-models Related Workmentioning

confidence: 99%

Ensemble-Based Deep Learning Model for Network Traffic Classification

Aouedi

Piamrat

Parrein

2022

IEEE Trans. Netw. Serv. Manage.

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Network Traffic Classification enables a number of practical applications ranging from network monitoring to resource management, with security implications as well. Nowadays, traffic classification has become a challenging task in order to distinguish among a variety of applications due to the huge amount of generated traffic. Therefore, developing Machine Learning (ML) models, which can successfully identify network applications, is one of the most important tasks. However, among the ML models applied to network traffic classification so far, no model outperforms all the others. To solve these issues, this paper proposes a novel Deep Learning (DL)-based approach that incorporates multiple Decision Tree based models. This approach employs a non-linear blending ensemble method by combining tree-based classifiers through DL in order to maximize generalization accuracy. This ensemble consists of two levels called base classifiers and meta-classifiers. In the first level, Decision Tree-based models are used as the base classifiers while in the second level, DL is used as a meta-model to combine the outputs of the base classifiers. Using two publicly available datasets, we show that our proposed ensemble is suitable for network traffic classification and outperforms the linear blending (using logistic regression as meta-model) as well as several well-known ML models, which are Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Multi-Layer Perceptron (MLP), AdaBoost, K-Nearest Neighbors (KNN), LightGBM, Catboost, and XGBoost.

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Intelligent Traffic Management in Next-Generation Networks

Cited by 23 publications

References 121 publications

An Adaptive State Consistency Architecture for Distributed Software-Defined Network Controllers: An Evaluation and Design Consideration

An Adaptive State Consistency Architecture for Distributed Software-Defined Network Controllers: An Evaluation and Design Consideration

Smart Preventive Maintenance of Hybrid Networks and IoT Systems Using Software Sensing and Future State Prediction

Ensemble-Based Deep Learning Model for Network Traffic Classification

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