Empowering Innovation: Private 5G Network Integration with Edge AI

Abstract

This research develops an application for Software-Defined Networking (SDN) specifically for bandwidth management. It entails the full process of planning, creating, and putting into action a software program that uses SDN features to efficiently distribute and manage bandwidth resources within a network. Private 5G networks prioritize reliability, ensuring consistent connectivity vital for AI applications dependent on a continuous data flow. Insights. Integration with edge computing allows AI processing to be closer to data sources, reducing latency and enhancing efficiency, particularly in applications requiring real-time insights. This study guarantees optimal network performance and operational efficiency by offering insightful information that helps make strategic decisions that are in line with needs. Software-defined networking (SDN) and network function virtualization (NFV) play crucial roles in private 5G networks. SDN facilitates network slicing that can be created to cater to various use cases and industries within a single physical infrastructure. At the same time, NFV complements network slicing by enabling the deployment of specific VNFs in each network slice, ensuring that the required network functions are available based on the slice's characteristics. This research uses various tools and platforms, such as Minnet and Open Network Operating Systems (ONOS), to experiment. The outcome of this study will empower innovation and improve enterprise business operations by developing new edge AI use cases and applications.