5G has a very flexible network architecture due to virtualization and will come with various customisations based on different use cases. 5G also promises to provide intelligent networks with high bandwidth and low latency. One of the tradeoffs for this is the complexity of network monitoring and resource management of 5G; making availability, reliability and performance a challenge. The adoption of Software Defined Networking (SDN) and Network Function Virtualization (NFV) concepts ensure availability of network data and flexibility in architectural decisions for 5G. Because of the availability of data and advanced computing capabilities usage of ML (Machine Learning)/Artificial Intelligence (AI) can be envisaged in the control and management of 5G networks by predicting the load on the network. This article proposes a solution to integrate time-series based predictive analytics with 5G Core and shows a comparative study between two Time Series Forecasting Models-AutoRegressive Integrated Moving Average (ARIMA) and Facebook Prophet. Fraunhofer FOKUS Open5GCore is used as the reference 5G testbed toolkit for validating the proposal.
5G network is very flexible because of the two concepts Network Functions Virtualization (NFV) and the Software Defined Networks (SDN). There are various use cases for 5G technology and for different cases different configuration of the network will be needed. 5G Technology will bring intelligence within the network. The ability to support massive connectivity across diverse devices will result in enormous data volume within the 5G network. Continuous monitoring and traffic log analysis in such a complex architecture will not be sufficient to ensure availability and reliability within the network. The integration of data analytics within the 5G network can leverage the potential of automation. By introducing automation in the monitoring process better Quality of Services (QoS) can be achieved and analysing the network traffic load for better bandwidth utilization within the network. This article proposes a solution to integrate time series based analytics with 5G core and predicting any threats within the system which can lead to system failure. To validate the proposal Fraunhofer FOKUS Open5GCore toolkit is used.
With the wide adoption of edge compute infrastructures, an opportunity has arisen to deploy part of the functionality at the edge of the network to enable a localized connectivity service. This development is also supported by the adoption of “on-premises” local 5G networks addressing the needs of different vertical industries and by new standardized infrastructure services such as Mobile Edge Computing (MEC). This article introduces a comprehensive set of deployment options for the 5G network and its network management, complementing MEC with the connectivity service and addressing different classes of use cases and applications. We have also practically implemented and tested the newly introduced options in the form of slices within a standard-based testbed. Our performed validation proved their feasibility and gave a realistic perspective on their impact. The qualitative assessment of the connectivity service gives a comprehensive overview on which solution would be viable to be deployed for each vertical market and for each large-scale operator situation, making a step forward towards automated distributed 5G deployments.
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