RFlow<sup>+</sup>: An SDN-based WLAN monitoring and management framework

Jang, Rhongho; Cho, DongGyu; Noh, Youngmin; Nyang, DaeHun

doi:10.1109/infocom.2017.8056995

Cited by 25 publications

(15 citation statements)

References 16 publications

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“…Jang et al propõem o sistema RFlow + [Jang et al, 2017]. Os autores argumentam que a ausência de mecanismos inteligentes e que possam tomar ações rápidas nas redes sem fio associada a ferramentas de monitoramento de tráfego que geram visões limitadas sobre a rede prejudicam a confiabilidade das redes sem fio.…”

Section: Trabalhos Relacionadosunclassified

“…No entanto, essa mesma popularização fomenta o desafio de controlar e gerenciar esse tipo de rede, já que um ponto de acesso típico está na vizinhança de diversos outros, sofrendo e gerando interferência [Biswas et al, 2015]. A ausência de mecanismos de inteligência e de ação rápida na rede, muitas vezes associada a visões limitadas de ferramentas de controle por fluxo, prejudicam a qualidade de experiência (QoE) de usuários de redes sem fio de grande escala [Jang et al, 2017].…”

Section: Introductionunclassified

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Uma Abordagem Não Supervisionada para Inferir Qualidade de Experiência em Redes Sem Fio de Grande Escala

Mattos¹,

Medeiros²,

Fernandes³

et al. 2019

Anais Do Workshop De Gerência E Operação De Redes E Serviços

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Inferir a qualidade da experiência de usuários de redes sem fio é desafiador, pois o monitoramento da rede não captura a qualidade para cada usuário individualmente. Este artigo propõe uma abordagem não supervisionada, baseada em aprendizado de máquina, para inferir a qualidade de experiência de diferentes perfis de uso de uma rede sem fio de grande escala. A abordagem proposta usa a correlação entre dados de uso de pontos de acesso e estatísticas de fluxos de dados na rede. A ideia central da proposta é coletar dados de utilização de diversos pontos de acesso, correlacioná-los com as estatísticas dos fluxos das conexões que passam pelos pontos de acesso, reportados pelo NetFlow, e, a partir da aplicação do algoritmo de agrupamento k-means, inferir diferentes perfis de uso da rede. A abordagem proposta foi avaliada na rede sem fio real de grande escala e os resultados mostram que a separação dos fluxos em cinco agrupamentos permite identificar perfis característicos de estados degradados da rede e situações de sobrecarga em pontos de acesso, considerando apenas as estatísticas de fluxos reportadas.

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Section: Trabalhos Relacionadosunclassified

Section: Introductionunclassified

Uma Abordagem Não Supervisionada para Inferir Qualidade de Experiência em Redes Sem Fio de Grande Escala

Mattos¹,

Medeiros²,

Fernandes³

et al. 2019

Anais Do Workshop De Gerência E Operação De Redes E Serviços

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“…To develop FlowRegulator, we utilize the recyclable counter with confinement (RCC) [4] that already has online decoding capability, and proven to be useful for measurement in the wireless SDN environment [2], [3]. To investigate its feasibility, we have tested RCC for its rate regulation (defined as Output ips/Input pps).…”

Section: Flowregulator Designmentioning

confidence: 99%

A cost-effective anomaly detection system using in-DRAM working set of active flows table

Jang

Moon

Noh

et al. 2019

Proceedings of the 12th Conference on Security and Privacy in Wireless and Mobile Networks

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In the zettabyte era, per-flow measurement becomes more challenging owing to the growth of both traffic volumes and the number of flows. Also, swiftness of detection of anomalies (e.g., DDoS attack, congestion, link failure, and so on) becomes paramount. For fast and accurate anomaly detection, managing an accurate working set of active flows (WSAF) from massive volumes of packet influxes at line rates is a key challenge. WSAF is usually located in a very fast but expensive memory, such as TCAM or SRAM, and thus the number of entries to be stored is quite limited. To cope with the scalability issue of WSAF, we propose to use In-DRAM WSAF with scales, and put a compact data structure called FlowRegulator in front of WSAF to compensate for DRAM's slow access time by substantially reducing massive influxes to WSAF without compromising measurement accuracy. We prototype and evaluated our system in a large scale real-world experiment (connected to monitoring port of our campus main gateway router for 113 hours, and capturing 122.3 million flows). As one key application, FlowRegulator detected heavy hitters with 99.8% accuracy.

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“…This trend has given rise to ambitious SDN-based approaches for Wireless Local Area Networks (WLANs) as a way not only to simplify network management but also to alleviate the complexity of algorithms aiming to optimize different aspects of the network [11][12][13]. As a result, a huge body of literature can be found on improvements on top of the Medium Access Control (MAC) layer, including the Modulation and Coding Scheme (MCS) [14,15] and resource-based slicing [16,17], among others.…”

Section: Introductionmentioning

confidence: 99%

Adaptive ML-Based Frame Length Optimisation in Enterprise SD-WLANs

2020

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Software-Defined Networking (SDN) is gaining a lot of traction in wireless systems with several practical implementations and numerous proposals being made. Despite instigating a shift from monolithic network architectures towards more modulated operations, automated network management requires the ability to extract, utilise and improve knowledge over time. Beyond simply scrutinizing data, Machine Learning (ML) is evolving from a simple tool applied in networking to an active component in what is known as Knowledge-Defined Networking (KDN). This work discusses the inclusion of ML techniques in the specific case of Software-Defined Wireless Local Area Networks (SD-WLANs), paying particular attention to the frame length optimization problem. With this in mind, we propose an adaptive ML-based approach for frame size selection on a per-user basis by taking into account both specific channel conditions and global performance indicators. By relying on standard frame aggregation mechanisms, the model can be seamlessly embedded into any Enterprise SD-WLAN by obtaining the data needed from the control plane, and then returning the output back to this in order to efficiently adapt the frame size to the needs of each user. Our approach has been gauged by analysing a multitude of scenarios, with the results showing an average improvement of 18.36% in goodput over standard aggregation mechanisms.

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RFlow⁺: An SDN-based WLAN monitoring and management framework

Cited by 25 publications

References 16 publications

Uma Abordagem Não Supervisionada para Inferir Qualidade de Experiência em Redes Sem Fio de Grande Escala

Uma Abordagem Não Supervisionada para Inferir Qualidade de Experiência em Redes Sem Fio de Grande Escala

A cost-effective anomaly detection system using in-DRAM working set of active flows table

Adaptive ML-Based Frame Length Optimisation in Enterprise SD-WLANs

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RFlow+: An SDN-based WLAN monitoring and management framework

Cited by 25 publications

References 16 publications

Uma Abordagem Não Supervisionada para Inferir Qualidade de Experiência em Redes Sem Fio de Grande Escala

Uma Abordagem Não Supervisionada para Inferir Qualidade de Experiência em Redes Sem Fio de Grande Escala

A cost-effective anomaly detection system using in-DRAM working set of active flows table

Adaptive ML-Based Frame Length Optimisation in Enterprise SD-WLANs

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Product

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About

RFlow⁺: An SDN-based WLAN monitoring and management framework