Recent Advances in Data Engineering for Networking

Zeydan, Engin; Mangues‐Bafalluy, Josep

doi:10.1109/access.2022.3162863

Cited by 8 publications

(5 citation statements)

References 151 publications

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“…Several studies have addressed various aspects of network management, orchestration and integration of AI/ML techniques [7], [9], [10], [11], [12], [13], [14], [15], [16], [2]. Several European Commission-funded Horizon 2020 and other initiatives, such as FlexNGIA 1 (aims to design a novel internet architecture for the next-generation tactile internet that leverages recent technological advances in virtualization, network softwarization and AI) and projects such as 5G-MONARCH 2 (focusing on intra-slice and cross-slice controllers to enable reprogrammability and functional reconfiguration of slices), SliceNet 3 (focusing on cognitive mechanisms to achieve dynamic slice reconfiguration) integrate state-of-the-art techniques and standardization architectures from SDOs such as 3GPP [17], ETSI [18], [19], ITU-T (FG-ML5G group) to create new architectures or improve existing ones. The integration of AI/ML into the architectures of these SDOs is an area of active research [20], [21], [22].…”

Section: Related Work and Monb5g Vision A Related Workmentioning

confidence: 99%

“…The aim is to provide innovative network services and management to meet the requirements of future applications [20]. A data engineering perspective for AI/ML-based standardization architectures can be found in [3]. However, these efforts lack scalable approaches for providing automated management and orchestration mechanisms that leverage distributed AI-based algorithms (e.g.…”

Section: Related Work and Monb5g Vision A Related Workmentioning

confidence: 99%

“…Unfortunately, a centralized solution is also impractical in terms of scalability and sustainability to meet the needs of such emerging applications [2]. Therefore, it is imperative to reassess and revise the existing standardization solutions for network infrastructure management and orchestration to overcome the limitations by effectively leveraging the recent advances such as Artificial Intelligence (AI)/Machine Learning (ML), Software Defined Networks (SDN) or Network Function Virtualization (NFV) [3]. A large number of services (thanks to the advances in SDN and NFV paving the way for the network slicing concept) require Life Cycle Management (LCM) of network slices with minimum human interaction and a reduced reaction time in case of service degradation [4].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Approach for Scalable and Sustainable 6G Networks

Blanco,

Zeydan,

Barrachina-Muñoz

et al. 2024

IEEE Open J. Commun. Soc.

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Hierarchical, distributed, scalable and Artificial Intelligence (AI)-based management of a massive number of network slices in different domains with the goal of zero-touch management is a major challenge for 6G networks. In this paper, we first propose a new vision for distributed network management and orchestration based on existing standardization architectures. This vision aims to embed AI/Machine Learning (ML) into the AI/ML architectures of Standardization Development Organizations (SDOs) such as the 3rd Generation Partnership Project (3GPP), the European Telecommunications Standards Institute (ETSI) and the International Telecommunication Union (ITU). Our second contribution is a numerical comparison of the benefits of the proposed distributed management and orchestration approach in terms of energy savings through Federated Learning (FL). The experimental topology includes a sophisticated infrastructure with VR streaming clients and servers, a monitoring system (MS), core network elements, aggregation server for federated learning (FL) and analytics engines (AEs). The deployment uses Kubernetes (K8s) and a top orchestrator that works together with an AI/ML model tailored to the envisioned use case. Experimental studies emulating the demanding Virtual Reality (VR) video streaming have demonstrated the effectiveness of the MonB5G framework in optimizing resource management, reducing overhead and improving energy efficiency. In particular, when convergence is achieved, the monitoring overhead is reduced by more than 11 times compared to the centralised SLA-constrained algorithm, along with data-driven management systems. This led to a more than 10-fold improvement in energy efficiency. At the end of the paper, we also discuss experimental results, VR video streaming specific challenges, scalability considerations and lessons learned throughout the implementation.

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Section: Related Work and Monb5g Vision A Related Workmentioning

confidence: 99%

Section: Related Work and Monb5g Vision A Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Approach for Scalable and Sustainable 6G Networks

Blanco,

Zeydan,

Barrachina-Muñoz

et al. 2024

IEEE Open J. Commun. Soc.

Self Cite

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“…This dataset was selected because of its data classification over social media content, having very close proximity to the data extracted from twitter. A combination of libraries was applied to the machine learning processing, using the framework Torch as the base of the process, the Tez library to provide the model function (Zeydan and Mangues-Bafalluy, 2022), the scikit learn library to generate the metrics during the training process and the Transformers library to provide the architecture, the optimizer and the BERT framework to NLP machine learning training (Devlin et al, 2018). Those libraries were selected to simplify the process of deep learning, bringing functions that turn the process easier than using straight TensorFlow.…”

Section: Proposed Approachmentioning

confidence: 99%

Sentiment Identification on Tweets to Forecast Cryptocurrency’s Volatility

Araújo¹,

Pinto²,

Ferrandin³

2023

Journal of Computer Science

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Cryptocurrencies have had a huge presence on social media since their creation. In current days, the constant increase of the mass of data produced by this environment has attracted several researchers to try to identify patterns with the potential to allow identification of the volatility in the crypto market before it happens. This approach involves the concept of the wisdom of the crowds, a popular theory in the economy field that in the current days may have the perfect tools to prove itself true. This scenario creates an opportunity to unite two new technologies, social media, and cryptocurrencies to the newest Natural Language Processing (NLP) tools, and produces a study in a rich and unexplored field. Executing a detailed sentiment analysis, this study intents to analyze the forecast of the volatility of cryptocurrencies through the detection and evaluation of several categories of sentiments on messages on twitter when it is associated with a specific cryptocurrency. To achieve this, an NLP model was trained with the GoEmotions dataset to identify and categorize emotions, and results were used to calculate the forecast of the cryptocurrency. Index terms cryptocurrency, social media, Natural Language Processing (NLP), GoEmotions.

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“…Hence, the service providers of large-scale IoT networks need to move the network traffic from IoT devices to the cloud for analytics [7]. It demands a scalable data pipeline to collect, analyze, and store the ever-growing network traffic streams from geographically distributed IoT devices [8].…”

Section: Introductionmentioning

confidence: 99%

UASDAC: An Unsupervised Adaptive Scalable DDoS Attack Classification in Large-Scale IoT Network Under Concept Drift

Selvam,

Maheswari Balasubramanian

2024

IEEE Access

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Day by day, the number of devices in IoT networks is increasing, and concurrently, the size of botnets in IoT networks is also expanding. Currently, attackers prefer IoT-based botnets to launch DDoS attacks, as IoT devices offer a vast attack surface. Many researchers have proposed machine and deep learning-based classifiers to classify DDoS and benign network traffic in online streams from IoT devices. However, the performance of the traditional machine and deep learning algorithms deteriorates when sudden concept or data drift occurs in the online streams and the volume and velocity of IoT network traffic increases. To address these challenges, we propose UASDAC, an adaptive and scalable data pipeline designed specifically to handle concept drift and detect DDoS traffic in real-time in massive online streams originating from IoT devices. UASDAC incorporates three key components: an online network stream collector for data collection, an online network stream analyzer with an unsupervised drift detector for detecting drift and DDoS traffic, and an online network stream repository for storing streams for future analytics. UASDAC leverages big data technologies to implement all the three components to achieve scalability. Additionally, UASDAC introduces an effective and efficient retraining technique to adapt to novel patterns in online streams in the presence of concept drift. We evaluated the performance of UASDAC in different concept drift scenarios using the benchmark dataset NSL-KDD and the latest IoT dataset IoT23. Our results demonstrate that UASDAC effectively identifies DDoS traffic in the presence of concept drift, achieving an accuracy range of 99.7% to 99.9%.

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Recent Advances in Data Engineering for Networking

Cited by 8 publications

References 151 publications

A Novel Approach for Scalable and Sustainable 6G Networks

A Novel Approach for Scalable and Sustainable 6G Networks

Sentiment Identification on Tweets to Forecast Cryptocurrency’s Volatility

UASDAC: An Unsupervised Adaptive Scalable DDoS Attack Classification in Large-Scale IoT Network Under Concept Drift

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