Machine-Learning Based Routing Pre-plan for SDN

Chen, Fengqing; Zheng, Xianghan

doi:10.1007/978-3-319-26181-2_14

Cited by 9 publications

(10 citation statements)

References 28 publications

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“…Also, an increase in runtime performance was observed when compared with the Q-learning algorithm. An ML based routing preplan solution for an SDN environment is presented in [69], considering (i) flow feature extraction, (ii) user requirement prediction, and (iii) route selection. Under the SDN route planning context, the core idea is to predict the user's business requirements and then plan ahead and set up routing policies, with a view of reducing delay effects.…”

Section: Machine Learningmentioning

confidence: 99%

Softwarization of Mobile Network Functions towards Agile and Energy Efficient 5G Architectures: A Survey

Dlamini

Rossi

Munaretto³

2017

Wireless Communications and Mobile Computing

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Future mobile networks (MNs) are required to be flexible with minimal infrastructure complexity, unlike current ones that rely on proprietary network elements to offer their services. Moreover, they are expected to make use of renewable energy to decrease their carbon footprint and of virtualization technologies for improved adaptability and flexibility, thus resulting in green and self-organized systems. In this article, we discuss the application of software defined networking (SDN) and network function virtualization (NFV) technologies towards softwarization of the mobile network functions, taking into account different architectural proposals. In addition, we elaborate on whether mobile edge computing (MEC), a new architectural concept that uses NFV techniques, can enhance communication in 5G cellular networks, reducing latency due to its proximity deployment. Besides discussing existing techniques, expounding their pros and cons and comparing state-of-the-art architectural proposals, we examine the role of machine learning and data mining tools, analyzing their use within fully SDN-and NFV-enabled mobile systems. Finally, we outline the challenges and the open issues related to evolved packet core (EPC) and MEC architectures.

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Section: Machine Learningmentioning

confidence: 99%

Softwarization of Mobile Network Functions towards Agile and Energy Efficient 5G Architectures: A Survey

Dlamini

Rossi

Munaretto³

2017

Wireless Communications and Mobile Computing

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“…Semi-supervised learning [178][179][180][181] was also used in SDN, but much less common compared with other learning approaches. The research was focused on traffic classification [178,180], routing [179] and intrusion detection [180]. Wang et al [178] proposed a new framework for QoS-aware traffic classification based on semisupervised learning.…”

Section: Semi-supervised Learning In Sdnmentioning

confidence: 99%

“…Chen and Zheng [179] introduced an efficient routing pre‐design solution based on semi‐supervised approach. The study suggested using an appropriate clustering algorithm such as Gaussian mixture model and k‐means clustering for feature extraction.…”

Section: Artificial Intelligence In Sdnmentioning

confidence: 99%

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Artificial intelligence enabled software‐defined networking: a comprehensive overview

2019

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Software-defined networking (SDN) represents a promising networking architecture that combines central management and network programmability. SDN separates the control plane from the data plane and moves the network management to a central point, called the controller that can be programmed and used as the brain of the network. Recently, the research community has shown an increased tendency to benefit from the recent advancements in the artificial intelligence (AI) field to provide learning abilities and better decision making in SDN. In this study, the authors provide a detailed overview of the recent efforts to include AI in SDN. The study showed that the research efforts focused on three main sub-fields of AI namely: machine learning, meta-heuristics and fuzzy inference systems. Accordingly, in this work, the authors investigate their different application areas and potential use, as well as the improvements achieved by including AI-based techniques in the SDN paradigm.

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“…The software defined networking along with the open flow monitor creates a more flexible and an improved network structure in the research for the developing efficient network operations [4]. Further the software defined networking with the slight modification incorporating the deep learning models enable the routing to be automatically adaptable to the traffic density [11], the computation overload caused by the traditional software defined networking that processes and configures routing methods for each flow that is new is overcome by the machine learning process, that enables pre-design solution based on the flow feature extraction, route selection and the requirement prediction, enhancing the performance of the routing with the software defined networking [14].…”

Section: Introductionmentioning

confidence: 99%

Improved Software-Defined Network Routing for Telecommunication Networks

Suma¹

2019

JUCCT

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The rapid advancements in the telecommunication networks, has led to the day by day progress in the data communication leading to the inclusion of many devices that causes complexities in managing and the maintaining of the networks. The outgrowing number of new network devices makes the traditional telecommunication networks incompatible to their flexible operation and the management. So the trending software defined networking can be opted for the provision of more convenient service providing a seamless communication, but the SDN’s lags in the self-adaptability and the efficient usage of the resources as it uses the concept of the traditional networks so the paper proposes an modified method of software defined networking based on the deep learning to enhance the performance, of the telecommunication networks. Further the evaluation of the telecommunication network routing with the improvised SDN, on the packet loss rate and the average delay shows that the proposed method is compatible for the seamless information provision of the nowadays telecommunication networks.

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Machine-Learning Based Routing Pre-plan for SDN

Cited by 9 publications

References 28 publications

Softwarization of Mobile Network Functions towards Agile and Energy Efficient 5G Architectures: A Survey

Softwarization of Mobile Network Functions towards Agile and Energy Efficient 5G Architectures: A Survey

Artificial intelligence enabled software‐defined networking: a comprehensive overview

Improved Software-Defined Network Routing for Telecommunication Networks

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