NELLY: Flow Detection Using Incremental Learning at the Server Side of SDN-Based Data Centers

Estrada-Solano, Felipe; Rendón, Óscar Mauricio Caicedo; Fonseca, Nelson L. S. da

doi:10.1109/tii.2019.2947291

Cited by 22 publications

(17 citation statements)

References 26 publications

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“…In addition, the introduction of blockchain to IIoT critical infrastructures can improve the interoperability (i.e., reciprocal operations) among different IIoT systems. Moreover, the massive data generated in IIoT can be used to identify the performance bottlenecks or abnormal activities so as to improve the scalability of IIoT [181]. In the future, the fusion of AI with the above technologies can further improve the scalability and elasticity of IIoT ecosystems.…”

Section: Scalable Critical Infrastructuresmentioning

confidence: 99%

Convergence of Blockchain and Edge Computing for Secure and Scalable IIoT Critical Infrastructures in Industry 4.0

Dai

Wang

2021

IEEE Internet Things J.

206

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Critical infrastructure systems are vital to underpin the functioning of a society and economy. Due to ever-increasing number of Internet-connected Internet-of-Things (IoTs) / Industrial IoT (IIoT), and high volume of data generated and collected, security and scalability are becoming burning concerns for critical infrastructures in industry 4.0. The blockchain technology is essentially a distributed and secure ledger that records all the transactions into a hierarchically expanding chain of blocks. Edge computing brings the cloud capabilities closer to the computation tasks. The convergence of blockchain and edge computing paradigms can overcome the existing security and scalability issues. In this paper, we first introduce the IoT/IIoT critical infrastructure in industry 4.0, and then we briefly present the blockchain and edge computing paradigms. After that, we show how the convergence of these two paradigms can enable secure and scalable critical infrastructures. Then, we provide a survey on state-of-the-art for security and privacy, and scalability of IoT/IIoT critical infrastructures. A list of potential research challenges and open issues in this area is also provided, which can be used as useful resources to guide future research.

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Section: Scalable Critical Infrastructuresmentioning

confidence: 99%

Convergence of Blockchain and Edge Computing for Secure and Scalable IIoT Critical Infrastructures in Industry 4.0

Dai

Wang

2021

IEEE Internet Things J.

206

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“…Promising results have been shown with good performance on traffic classification (F1-score: >99%) under different workloads and load levels in various scenarios (browsing, email, FTP, P2P, Youtube, Spotify). Estrada-Solano et al [19] proposed NELLY, which leverages incremental learning from software-defined networking (SDN) to identify elephant flows of great magnitude in the network accurately in a reasonable time while generating low control overhead. NELLY aims to address the inaccuracy, high overhead and poor scalability of flow detection in software-defined data center networks.…”

Section: Traffic Classification and Schedulingmentioning

confidence: 99%

Next-Generation Data Center Network Enabled by Machine Learning: Review, Challenges, and Opportunities

Dong¹,

Munir

Tout

et al. 2021

IEEE Access

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Data center network (DCN) is the backbone of many emerging applications from smart connected homes to smart traffic control and is continuously evolving to meet the diverse and ever-increasing computing requirements of these applications. The data centers often have tens of thousands of components such as servers and switches/routers that work together to achieve a common objective and serve these applications. Managing such large data centers is a tedious process and demands for automation, intelligent control and decision making within the data center. Recently both the industry and academia have focused on bringing intelligence to the control, automation and management of DCNs. Despite the variety of works that surveyed ML for networking, to the best of our knowledge, none has focused on DCN, which makes this survey original. Readers in the academic and industrial communities will all benefit from a comprehensive discussion of the ML solutions applied in DCN to address critical essential problems, including workload forecasting, traffic control and optimization, topology management, network state prediction and failures analysis, and security. Furthermore, this article outlines the challenges and concludes with the future research venues in adopting ML for automatic, intelligent and autonomous DCNs.

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“…Em Estrada-Solano et al (2019) foi realizado um trabalho criando a ferramenta NELLY, que utiliza aprendizado incremental junto de SDN para identificar fluxos de grande magnitude na rede, chamados de fluxos elefante, sem criar mais tráfego na rede. Fu et al (2020) utiliza Q-Learning para gerar rotas ideias para evitar congestionamento em redes de data centers que utilizam SDN.…”

Section: Trabalhos Relacionadosunclassified

Controle de Congestionamento em Data Center baseado em SDN e Aprendizado de Máquina: uma Proposta Preliminar

Diel

Koslovski

2020

Anais Da XVIII Escola Regional De Redes De Computadores (ERRC 2020)

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Com novos serviços surgindo diariamente e cada vez mais dados sendo gerados e transferidos pela Internet, e por se tratar de um recurso ubíquo e compartilhado, o congestionamento em redes de computadores é um problema real e comum. Sobretudo, o congestionamento de redes de comunicação de data centers afeta o desempenho das aplicações e a qualidade do serviço. O presente trabalho discute uma possível solução para amenizar congestionamentos em data centers utilizando os recursos oferecidos por redes definidas por software e aprendizado de máquina.

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NELLY: Flow Detection Using Incremental Learning at the Server Side of SDN-Based Data Centers

Cited by 22 publications

References 26 publications

Convergence of Blockchain and Edge Computing for Secure and Scalable IIoT Critical Infrastructures in Industry 4.0

Convergence of Blockchain and Edge Computing for Secure and Scalable IIoT Critical Infrastructures in Industry 4.0

Next-Generation Data Center Network Enabled by Machine Learning: Review, Challenges, and Opportunities

Controle de Congestionamento em Data Center baseado em SDN e Aprendizado de Máquina: uma Proposta Preliminar

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