5 Growth: Secure and Reliable Network Slicing for Verticals

Cunha, Vitor A.; Maroulis, Nikolaos; Papagianni, Chrysa; Sacido, Javier; Jimenez, Manuel A.; Ubaldi, Fabio; Gharbaoui, M.; Chang, Chia Yu; Koursioumpas, Nikolaos; Tomakh, Konstantin; Corujo, Daniel; Barraca, João Paulo; Barmpounakis, Sokratis; Kucherenko, D.; Giorgetti, A.; Boddi, Andrea; Valcarenghi, L.; Kolodiazhnyi, O.; Zabala, Aitor; García‐Saavedra, Andrés

doi:10.1109/eucnc/6gsummit51104.2021.9482536

Cited by 10 publications

(9 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Cunha et al [ 75 ] and Chang et al [ 76 , 77 ] present a solution that aims to ensure performance isolation for network slicing, specifically with regards to bandwidth and delay guarantees, in order to support three Industry 4.0 case scenarios, such as digital twin, telemetry, and remote support. The proposed implementation integrates P4- and OpenFlow-based switches at the transport network data plane.…”

Section: Findings and Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Programmable Data Plane Applications in 5G and Beyond Architectures: A Systematic Review

Brito,

Moreno,

Contreras

et al. 2023

Sensors

View full text Add to dashboard Cite

The rapid evolution of 5G and beyond technologies has sparked an unprecedented surge in the need for networking infrastructure that can deliver high speed, minimal latency, and remarkable flexibility. The programmable data plane, which enables the dynamic reconfiguration of network functions and protocols, is becoming increasingly important in meeting these requirements. This paper provides an overview of the current state of the art in programmable data planes implemented in 5G and beyond architectures. It proposes a classification of the reviewed studies based on system architecture and specific use cases. Furthermore, the article surveys the primary applications of programmable devices in emerging telecommunication networks, such as tunneling and forwarding, network slicing, cybersecurity, and in-band telemetry. Finally, this publication summarizes the open research challenges and future directions. In addition to offering a comprehensive review of programmable data plane applications in telecommunication networks, this article aims to guide further research in this promising field for network operators and researchers alike.

show abstract

Section: Findings and Discussionmentioning

confidence: 99%

“…Regarding throughput, experiments in [ 60 , 69 , 75 ] display 98.7 Gbps, 40 Gbps, and 2 Gbps, respectively, while [ 47 ] achieves approximately 100 Gbps. In [ 80 ], the obtained data rate is 402× higher than that of a pure UPF software design.…”

Section: Findings and Discussionmentioning

confidence: 99%

Programmable Data Plane Applications in 5G and Beyond Architectures: A Systematic Review

Brito,

Moreno,

Contreras

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Analyzing the network behavior (e.g., based on KPIs such as packet loss and downlink delay) is a management task during the network slice operation phase. In [88], the authors implemented an AI-based module for assisting administrators in detecting anomalies among services in slices deployed on a virtualized infrastructure. The solution of the authors, aiming to classify network traffic, has three phases: (i) pre-processing and feature selection, (ii) clustering, and (iii) anomaly detection.…”

Section: Other Investigationsmentioning

confidence: 99%

Survey on Machine Learning-Enabled Network Slicing: Covering the Entire Life Cycle

Donatti¹,

Corrêa

Martins

et al. 2024

IEEE Trans. Netw. Serv. Manage.

View full text Add to dashboard Cite

Network slicing (NS) is becoming an essential element of service management and orchestration in communication networks, starting from mobile cellular networks and extending to a global initiative. NS can reshape the deployment and operation of traditional services, support the introduction of new ones, vastly advance how resource allocation performs in networks, and notably change the user experience. Most of these promises still need to reach the real world, but they have already demonstrated their capabilities in many experimental infrastructures. However, complexity, scale, and dynamism are pressuring for a Machine Learning (ML)-enabled NS approach in which autonomy and efficiency are critical features. This trend is relatively new but growing fast and attracting much attention. This article surveys Artificial Intelligence-enabled NS and its potential use in current and future infrastructures. We have covered state-of-the-art ML-enabled NS for all network segments and organized the literature according to the phases of the NS life cycle. We also discuss challenges and opportunities in research on this topic.

show abstract

“…Fig. 9 shows the detection performance comparison between the BiWGAN-GP algorithm and the state-of-the-art GAN algorithms, including GAN [20], WGAN [21], WGAN-GP [23] and BiGAN [22]. For convenience, the standalone training mode is used for all the five algorithms in this simulation.…”

Section: Effectivenessmentioning

confidence: 99%

“…Thirdly, substrate networks consist of multiple regions and VMs that constitute a network slice can cross multiple regions, so resource metrics data of VMs are distributed in the networks. Existing works commonly use a centralized database to aggregate all local metrics data, such as Gnocchi [20] and Prometheus [21], [22], which will compromise the communication and computation efficiency when learning a global VM anomaly detection model.…”

Section: Introductionmentioning

confidence: 99%

Federated Multi-Discriminator BiWGAN-GP Based Collaborative Anomaly Detection for Virtualized Network Slicing

Wang

Liang

Lun

et al. 2022

IEEE Trans. on Mobile Comput.

View full text Add to dashboard Cite

Virtualized network slicing allows a multitude of logical networks to be created on a common substrate infrastructure to support diverse services. A virtualized network slice is a logical combination of multiple virtual network functions, which run on virtual machines (VMs) as software applications by virtualization techniques. As the performance of network slices hinges on the normal running of VMs, detecting and analyzing anomalies in VMs are critical. Based on the three-tier management framework of virtualized network slicing, we first develop a federated learning (FL) based three-tier distributed VM anomaly detection framework, which enables distributed network slice managers to collaboratively train a global VM anomaly detection model while keeping metrics data locally. The high-dimensional, imbalanced, and distributed data features in virtualized network slicing scenarios invalidate the existing anomaly detection models. Considering the powerful ability of generative adversarial network (GAN) in capturing the distribution from complex data, we design a new multi-discriminator Bidirectional Wasserstein GAN with Gradient Penalty (BiWGAN-GP) model to learn the normal data distribution from high-dimensional resource metrics datasets that are spread on multiple VM monitors. The multi-discriminator BiWGAN-GP model can be trained over distributed data sources, which avoids high communication and computation overhead caused by the centralized collection and processing of local data. We define an anomaly score as the discriminant criterion to quantify the deviation of new metrics data from the learned normal distribution to detect abnormal behaviors arising in VMs. The efficiency and effectiveness of the proposed collaborative anomaly detection algorithm are validated through extensive experimental evaluation on a real-world dataset.

show abstract

5 Growth: Secure and Reliable Network Slicing for Verticals

Cited by 10 publications

References 9 publications

Programmable Data Plane Applications in 5G and Beyond Architectures: A Systematic Review

Programmable Data Plane Applications in 5G and Beyond Architectures: A Systematic Review

Survey on Machine Learning-Enabled Network Slicing: Covering the Entire Life Cycle

Federated Multi-Discriminator BiWGAN-GP Based Collaborative Anomaly Detection for Virtualized Network Slicing

Contact Info

Product

Resources

About