CygNet MaSoN: Analytics and Machine Learning Enabled Management System for 5G Networks

R, Archana T; Deepika, V.; Kumar, Avinash; Ramachandran, Madanagopal; Sivalingam, Krishna M.; Koonampilli, J Babu Narayanan

doi:10.1109/comsnets51098.2021.9352830

Cited by 4 publications

(4 citation statements)

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“…The purpose of the ML algorithm is to understand the traffic activity and determine how the distribution of service flows should be made. This can be looked at as a classification problem or as a scheduling problem, and in both cases ML can provide quality algorithms and ML-enabled Management System for investigating traffic features in determining an outcome [26]. ML has experimentally established algorithms to address data divisions into recursive branches according to feature values, as are Decision Tree (where the branching ends with a predicted outcome), Random Forest (where there is an ensemble of decision trees making the predictions, in so that each tree has an independent result, but the final decision is a contribution from all the outcomes), and XGBoost (which is an implementation of gradient boosted decision trees, and is one of the most popular and best performing ML algorithms).…”

Section: System Model For Mec With Qos Supportmentioning

confidence: 99%

A MOBILE EDGE COMPUTING SERVICES WITH QoS SUPPORT MODEL FOR NEXT GENERATION MOBILE NETWORKS

Šuminoski,

Veličkovska,

Janevski

2022

JEEIT

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This paper presents a novel overview in intelligent multi-access QoS mobile edge computing (MEC) for beyond 5G networks and services. There are many challenges faced by the expansion of Cloud networks and Mobile networks, which can be solved by providing connectivity at the edge of the network, i.e. with Mobile Edge Computing networks. The MEC improves overall network performance and reduces end-to-end service delay. Also, the improved advanced QoS model including Machine Learning (ML) algorithm within for next generation of mobile networks and services are proposed. The purpose of the ML algorithm is to understand the traffic activity and determine how the traffic schedule should be made. Given a set of machines and a set of jobs, the model should compute the processing schedule that minimizes specified metrics. The proposed model combines the most powerful features of both Cloud and Edge computing, independent from any existing and future Radio Access Technology, leading to possible better performance utility networks, lower service delay with high QoS provisioning for many used multimedia service. Finally, this paper gives an overview of the existing Mobile Edge Computing technologies and several existing use cases. Undoubtedly, MEC with QoS support is an innovative network paradigm going in 6G, which can essentially answer many of the existing Mobile Networks' challenges.

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Section: System Model For Mec With Qos Supportmentioning

confidence: 99%

A MOBILE EDGE COMPUTING SERVICES WITH QoS SUPPORT MODEL FOR NEXT GENERATION MOBILE NETWORKS

Šuminoski,

Veličkovska,

Janevski

2022

JEEIT

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“…CygNet MaSoN is a management system providing the Element Management (EM) functionality for the network functions and also part of the Network Management functionality for 5G networks, as originally presented in brief in [5]. The MaSoN architecture and components are shown in MaSoN integrates with all the NFs over a Representational state transfer (RESTful) API, defined in 3GPP specifications, to collect all the management data across different management functions.…”

Section: A System Architecture and Componentsmentioning

confidence: 99%

“…This section describes three different 5G network use cases studied using the MaSoN system. The first two use cases have been briefly described in our earlier work [5]; in this paper, we describe detailed results for these use cases and a new use case.…”

Section: G Analytics/ml Use Casesmentioning

confidence: 99%

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5G Network Management System With Machine Learning Based Analytics

et al. 2022

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Application of intelligent data analytics using machine learning in management of 5G networks can enable autonomous networking capabilities in 5G networks. This paper describes the design and implementation of CygNet MaSoN, a management system supporting advanced aggregation and analytics features combined with machine learning. The system supports detection of anomalous network behaviour, detection of degradation in network performance and service quality and also supports resource optimization. The main objective is to achieve self-organizing and closed loop automation functionalities expected as part of autonomous functioning of 5G networks. Details of the system architecture and components are presented. Three real-life use cases implemented on this system are then described. The features considered, machine learning models built and synthetic data generation methods adopted are presented. The results obtained using the MaSoN system are also presented to demonstrate the effectiveness of the system in 5G network operations.

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A comprehensive systematic review on machine learning application in the 5G-RAN architecture: Issues, challenges, and future directions

Talal,

Garfan,

Qays

et al. 2025

Journal of Network and Computer Applications

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CygNet MaSoN: Analytics and Machine Learning Enabled Management System for 5G Networks

Cited by 4 publications

References 0 publications

A MOBILE EDGE COMPUTING SERVICES WITH QoS SUPPORT MODEL FOR NEXT GENERATION MOBILE NETWORKS

A MOBILE EDGE COMPUTING SERVICES WITH QoS SUPPORT MODEL FOR NEXT GENERATION MOBILE NETWORKS

5G Network Management System With Machine Learning Based Analytics

A comprehensive systematic review on machine learning application in the 5G-RAN architecture: Issues, challenges, and future directions

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