SDN Based End-to-End Inter-Domain Routing Mechanism for Mobility Management and Its Evaluation

Hata, Masaharu; Soylu, Mustafa; Izumi, Satoru; Abe, Toru; Suganuma, Takuo

doi:10.3390/s18124228

Cited by 8 publications

(4 citation statements)

References 12 publications

(13 reference statements)

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“…where H k p is the diagonal matrix of routing metrics of the pth domain links. The goal coordination principle [9][10][11]15,16] solves the optimization problem (19). Then, moving to the problem at the unconditional extremum, it is necessary to maximize by → µ the Lagrangian of the form:…”

Section: Methods Of Hierarchical Coordination Inter-domain Routing In Communication Network Under Provision Of Normalized Qosmentioning

confidence: 99%

“…Under analysis, some research results concern the multidomain SDN routing and specifics of softwarized architectures [13][14][15][16][17][18][19][20][21]. The most promising solutions are explained in Table 1.…”

Section: Research Backgroundmentioning

confidence: 99%

“…An analysis of existing works regarding hierarchical routing shows that the most promising approaches are grounded on flow-based models and optimization methods [15][16][17][22][23][24]. Particular attention should be paid to solutions related to principles and postulates of the hierarchical multilevel systems theory [25,26].…”

Section: Research Backgroundmentioning

confidence: 99%

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Advanced Tensor Solution to the Problem of Inter-Domain Routing with Normalized Quality of Service

et al. 2022

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The advanced tensor solution to the problem of inter-domain routing with normalized Quality of Service under hierarchical coordination in a communication network is proposed in the paper. The novelty of the method based on the tensor model lies in the more flexible load balancing over the network due to the presence of requirements to average end-to-end delay of packets. The framework of the method comprises a decomposed flow-based routing model that includes the inter-domain routing interaction conditions and ensures the normalized Quality of Service derived from the tensor model. Considering the mentioned above, the advanced inter-domain Quality of Service routing task was formulated in the optimization form, using the quadratic optimality criterion. The conducted analysis of the numerical research results confirmed the efficiency and adequacy of the proposed method when the desired solutions were obtained during the finite number of iterations under a provision of the normalized Quality of Service. It should be noted that the reduced number of such iterations during the operation of the method will decrease the amount of service traffic transmitted over the network needed for obtaining the final solution in the process of inter-domain routing with normalized Quality of Service.

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Section: Methods Of Hierarchical Coordination Inter-domain Routing In Communication Network Under Provision Of Normalized Qosmentioning

confidence: 99%

Section: Research Backgroundmentioning

confidence: 99%

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Advanced Tensor Solution to the Problem of Inter-Domain Routing with Normalized Quality of Service

et al. 2022

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“…In [47], the authors proposed a framework made on top of Mininet [48] and Quagga routing suite [49] that uses the centralization functions of SDN to linearly lessen the border gateway protocol's convergence time as well as churn rates. Authors in [50] suggested a method for optimizing the route in an SDN-based setting. Route optimization was based on different constraints such as bandwidth, flow operations, and number of domains available while a mobile node travels through the SDN-based structure.…”

Section: ) Inter-domain Routingmentioning

confidence: 99%

Intrusion Detection System in Software-Defined Networks Using Machine Learning and Deep Learning Techniques –A Comprehensive Survey

Rayhan¹,

Islam²,

Shatabda³

et al. 2022

Preprint

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<div>At present, the Internet is facing numerous attacks of different kinds that put its data at risk. The safety of information within the network is, therefore, a significant concern. In order to prevent the loss of incredibly valuable information, the Intrusion Detection System (IDS) was developed to recognize the outbreak of a stream of attacks and notify the network system administrator providing network security. IDS is an extrapolative model used to detect network traffic as routine or attack. Software-Defined Networks (SDN) is a revolutionary paradigm that isolates the control plane from the data plane, transforming the concept of a software-driven network. Through this data and control plane separation, SDN provides us the opportunity to create a manageable and programmable network, allowing applications in the top plane to access physical devices via the controller. The controller functioning inside the control plane executes network modules and establishes flow rules to forward packets in the switches residing in the data plane. Cyber attackers target the SDN controller to subdue the control plane, which is considered the brain of the SDN, providing a plethora of functionalities such as regulating flow control to switches or routers in the data plane below via southbound Application Programming Interfaces (APIs) and business and application logic in the application plane above via northbound APIs to implement sophisticated networks. However, the control plane becomes a tempting prospect for security attacks from adversaries because of its centralization feature. This paper includes an in-depth overview of the notable published articles from 2015 to 2021 that used Machine Learning (ML) and Deep Learning (DL) techniques to construct an IDS solution to provide security for SDN. We also present two detailed taxonomic studies regarding IDS, and ML-DL techniques based on their learning categories, exploring various IDS solutions to secure the SDN paradigm. We have also conducted brief research on a few benchmark datasets used to construct IDS in the SDN paradigm. To conclude the survey, we provide a discussion that sheds light on continuous challenges and IDS issues for SDN security.</div>

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SDN‐Based Session and Mobility Management in5GNetworks

2020

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The analysis performed in this article is a concise proposal on how SDN solutions can be integrated into 5G mobile networks in order to address key cellular network challenges related to session and mobility management. Initially, we present the key characteristics of SDN and how session and mobility management are performed in this type of networks. Then we present how session and mobility management are performed in 5G mobile networks and what are the potential drawbacks of the conventional designs that could lead to excessive signaling and/or delays. Then, we analyze how the mobility management can be enhanced using SDN in ultra‐dense 5G networks, where current solutions will lead to an explosion of the signaling cost. Furthermore, we analyze how handling of local traffic in 5G mobile networks using SDN will reduce the delays and the core network signaling in specific 5G use cases, that of fog computing and V2X communications.

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SDN Based End-to-End Inter-Domain Routing Mechanism for Mobility Management and Its Evaluation

Cited by 8 publications

References 12 publications

Advanced Tensor Solution to the Problem of Inter-Domain Routing with Normalized Quality of Service

Advanced Tensor Solution to the Problem of Inter-Domain Routing with Normalized Quality of Service

Intrusion Detection System in Software-Defined Networks Using Machine Learning and Deep Learning Techniques –A Comprehensive Survey

SDN‐Based Session and Mobility Management in5GNetworks

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