Data collection in SDN networks with contextual analysis

Miguel, Constantino; Neto, Francisco José Badaró Valente; Santos, Jorge A.; Sampaio, Paulo N. M.

doi:10.23919/cisti.2019.8760999

Cited by 2 publications

(3 citation statements)

References 9 publications

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“…The control plane can collect QoS data such as latency, throughput, packet loss rate, bandwidth utilization, etc. in order to make sure that network traffic is prioritized and delivered adhering to user requirements [378].…”

Section: Data Collectionmentioning

confidence: 99%

A Comprehensive Survey on Knowledge-Defined Networking

Wijesekara

Gunawardena

2023

Telecom

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Traditional networking is hardware-based, having the control plane coupled with the data plane. Software-Defined Networking (SDN), which has a logically centralized control plane, has been introduced to increase the programmability and flexibility of networks. Knowledge-Defined Networking (KDN) is an advanced version of SDN that takes one step forward by decoupling the management plane from control logic and introducing a new plane, called a knowledge plane, decoupled from control logic for generating knowledge based on data collected from the network. KDN is the next-generation architecture for self-learning, self-organizing, and self-evolving networks with high automation and intelligence. Even though KDN was introduced about two decades ago, it had not gained much attention among researchers until recently. The reasons for delayed recognition could be due to the technology gap and difficulty in direct transformation from traditional networks to KDN. Communication networks around the globe have already begun to transform from SDNs into KDNs. Machine learning models are typically used to generate knowledge using the data collected from network devices and sensors, where the generated knowledge may be further composed to create knowledge ontologies that can be used in generating rules, where rules and/or knowledge can be provided to the control, management, and application planes for use in decision-making processes, for network monitoring and configuration, and for dynamic adjustment of network policies, respectively. Among the numerous advantages that KDN brings compared to SDN, enhanced automation and intelligence, higher flexibility, and improved security stand tall. However, KDN also has a set of challenges, such as reliance on large quantities of high-quality data, difficulty in integration with legacy networks, the high cost of upgrading to KDN, etc. In this survey, we first present an overview of the KDN architecture and then discuss each plane of the KDN in detail, such as sub-planes and interfaces, functions of each plane, existing standards and protocols, different models of the planes, etc., with respect to examples from the existing literature. Existing works are qualitatively reviewed and assessed by grouping them into categories and assessing the individual performance of the literature where possible. We further compare and contrast traditional networks and SDN against KDN. Finally, we discuss the benefits, challenges, design guidelines, and ongoing research of KDNs. Design guidelines and recommendations are provided so that identified challenges can be mitigated. Therefore, this survey is a comprehensive review of architecture, operation, applications, and existing works of knowledge-defined networks.

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Section: Data Collectionmentioning

confidence: 99%

A Comprehensive Survey on Knowledge-Defined Networking

Wijesekara

Gunawardena

2023

Telecom

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“…O Módulo de Coleta de Dados usa os dados obtidos dos elementos de rede e da comunicac ¸ão fim-a-fim do tráfego dos servic ¸os para definir as métricas de QoS, QoD e QoE, necessárias para o cálculo do QoC (Qualidade de Contexto), conforme descrito em [Miguel et al 2019]. Estas métricas, quando analisadas em conjunto, definem um contexto de análise de dados mais amplo do que a análise de cada variável individualmente.…”

Section: Módulo De Coletaunclassified

“…Conforme descrito em [Miguel et al 2019], o cálculo de QoC é realizado para cada porta do elemento de rede, tendo como referência a saída do fluxo de dados. O resultado do cálculo sofre uma interpolac ¸ão de forma que seu resultado varie entre os números 1 a 5, coerentes com a Mean Opinion Score (MOS) [Neto et al 2018].…”

Section: Módulo De Coletaunclassified

Reprogramação de Fluxos de Dados em Redes SDN Baseada na Análise Contextual do Tráfego

Neto

Miguel

Sampaio

2021

Anais Do XXVI Workshop De Gerência E Operação De Redes E Serviços (WGRS 2021)

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Software-Defined Networking (SDN) é um paradigma de rede que permite a aplicação de novas técnicas de engenharia de tráfego através da separação do plano de controle do plano de encaminhamento. De forma a dar suporte às características dinâmicas do ambiente computacional (tais como, condições dinâmicas de tráfego e variedade protocolos de rede, mobilidade do usuário, heterogeneidade de dispositivos, entre outros), técnicas inovadoras de engenharia de tráfego devem ser propostas para lidar com aplicações sensíveis às variações de atraso, tais como videoconferência e voz sobre IP. Neste trabalho apresentamos o arcabouço Context-Aware Adaptive Routing Framework SDN (CAARF-SDN), uma solução para otimização do tráfego de redes, compatível com qualquer controlador SDN, através de uma abordagem dinâmica e proativa baseada em contexto. Neste trabalho, está aplicado a otimização de tráfego de voz abordando a mudança da prioridade do ﬂuxo e a mudança de topologia, baseando a noção de contexto nas métricas de qualidade de serviço, experiência e dispositivo (QoE, QoS e QoD) demonstrando a otimização dos recursos de rede em cenários adversos.

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Data collection in SDN networks with contextual analysis

Cited by 2 publications

References 9 publications

A Comprehensive Survey on Knowledge-Defined Networking

A Comprehensive Survey on Knowledge-Defined Networking

Reprogramação de Fluxos de Dados em Redes SDN Baseada na Análise Contextual do Tráfego

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