Autonomous Network Slicing Prototype Using Machine-Learning-Based Forecasting for Radio Resources

Salhab, Nazih; Langar, Rami; Rahim, Rana; Cherrier, Sylvain; Outtagarts, Abdelkader

doi:10.1109/mcom.001.2000922

Cited by 12 publications

(12 citation statements)

References 10 publications

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“…Taking current gaps on 5G experimental prototypes into account, Ref. [46] proposed a 5G micro-service-based prototype that is able to auto-configure radio resources for network slices with ML. Mainly, the authors focused on eMBB and mMTC types of slices.…”

Section: Machine Learning Applied To Network Slicingmentioning

confidence: 99%

Integration of Network Slicing and Machine Learning into Edge Networks for Low-Latency Services in 5G and beyond Systems

2022

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Fifth-generation (5G) and beyond networks are envisioned to serve multiple emerging applications having diverse and strict quality of service (QoS) requirements. To meet ultra-reliable and low latency communication, real-time data processing and massive device connectivity demands of the new services, network slicing and edge computing, are envisioned as key enabling technologies. Network slicing will prioritize virtualized and dedicated logical networks over common physical infrastructure and encourage flexible and scalable networks. On the other hand, edge computing offers storage and computational resources at the edge of networks, hence providing real-time, high-bandwidth, low-latency access to radio network resources. As the integration of two technologies delivers network capabilities more efficiently and effectively, this paper provides a comprehensive study on edge-enabled network slicing frameworks and potential solutions with example use cases. In addition, this article further elaborated on the application of machine learning in edge-sliced networks and discussed some recent works as well as example deployment scenarios. Furthermore, to reveal the benefits of these systems further, a novel framework based on reinforcement learning for controller synchronization in distributed edge sliced networks is proposed.

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Section: Machine Learning Applied To Network Slicingmentioning

confidence: 99%

Integration of Network Slicing and Machine Learning into Edge Networks for Low-Latency Services in 5G and beyond Systems

2022

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“…However, the traffic being transmitted through the slices is dynamic but the capacity that lies underneath is static, generating a conflict of under or overprovisioned capacity. Several authors have worked over this issue thoroughly [1], [2], [6]- [11].…”

Section: Related Work a Slicing Resource Predictionmentioning

confidence: 99%

“…On a different line, the authors of [1] and [2] use per-slice throughput prediction to assign radio network resources in the form of the Physical Resource Blocks (PRB) to predict the best wireless resource allocation. However, there is a considerable difference in the approach in terms of ML selected.…”

Section: Related Work a Slicing Resource Predictionmentioning

confidence: 99%

“…In [1] a web-based algorithm is presented, taking as input the number of available time-slots of the network and their renting costs. By contrast, in [2], the authors use a regressor tree with the PRB historically used per equipment, per slice type, and the currently requested PRB. However, both works perform the prediction offline.…”

Section: Related Work a Slicing Resource Predictionmentioning

confidence: 99%

“…At this point, there is a global trend that Artificial Intelligence (AI) and, in particular, Machine Learning (ML) needs to be incorporated into the future mobile networks to approximate such complex optimization functions. Current research has shown that ML models can be used to predict the behavior of different Key Performance Indicators (KPIs) based on past experiences and take autonomous decisions based on those predictions [1], [2]. In the case of slicing, ML can be used to predict the throughput that a slice is going to handle.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Design of AI-based Resource Forecasting Methods for Network Slicing

Camargo

Coronado

Gómez

et al. 2022

2022 International Wireless Communications and Mobile Computing (IWCMC)

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With the forthcoming of 5G networks, the underlying infrastructure needs to support a higher number of heterogeneous services with different QoS needs than ever. For that reason, 5G inherently provides a way to allocate these services over the same infrastructure through the concept of Network Slicing. However, to maximize revenue and reduce operational costs, a method to proactively adapt the resources assigned to each slice becomes imperative. For that reason, this work presents two Machine Learning (ML) models, leveraging Long-Short Term Memory (LSTM) and Random Forest algorithms, to forecast the throughput of each slice and adapt accordingly the amount of resources needed. The models are evaluated using NS-3, which has been integrated with the ML models through a shared memory framework. This enables a closed loop in which the predictions of the models can be used at run time to introduce changes in the network. Consequently, it makes it able to cope with the forecasted requirements, eliminating the need for off-line training and resembling better a real-life scenario. The evaluation performed shows the ability of the models to predict the slices' throughput under various settings and proves that Random Forest provides up to 26% better results than LSTM.

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DDoS Attacks Detection and Mitigation in 5G and Beyond Networks: A Deep Learning-based Approach

Bousalem

Silva

Langar

et al. 2022

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

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Network slicing, where a single physical network is partitioned into several fit-for-purpose virtual networks with different degrees of isolation and quality of service (QoS), is a key enabler of 5G and beyond mobile networks. However, it is prone to security threats such as Distributed Denial-of-Service (DDoS) attacks. In this paper, we propose a solution based on Deep Learning (DL) that detects such attacks, and then creates a sinkhole-type slice with a small portion of physical resources to isolate and mitigate the attackers' action. Using our 5G prototype based on OpenAirInterface, we evaluate our approach by comparing several DL models in terms of detection accuracy, false positive rate, execution time, among other Machine Learning-related metrics. We also assess the performance of created 5G network slices in terms of benign/malicious users' throughput, as well as the processing time during the slicing operations. Results show that our approach is able to detect DDoS attacks in a timely manner with an accuracy of almost 97% and a false positive rate of less than 4%. We also show that our approach decreases the network throughput for the malicious users by a factor of 15, while maintaining a high network throughput for benign users.

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Autonomous Network Slicing Prototype Using Machine-Learning-Based Forecasting for Radio Resources

Cited by 12 publications

References 10 publications

Integration of Network Slicing and Machine Learning into Edge Networks for Low-Latency Services in 5G and beyond Systems

Integration of Network Slicing and Machine Learning into Edge Networks for Low-Latency Services in 5G and beyond Systems

Design of AI-based Resource Forecasting Methods for Network Slicing

DDoS Attacks Detection and Mitigation in 5G and Beyond Networks: A Deep Learning-based Approach

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