Optimizing VNF Migration in B5G Core Networks: A Machine Learning Approach

Software-Defined Networking (SDN) is an innovative networking paradigm that fundamentally changes the way network management and operation are approached. Network Functions Virtualization (NFV) is a network architecture concept that leverages standard IT virtualization technology to virtualize entire classes of network node functions into building blocks that can be connected or chained together to create communication services. NFV is part of the broader trend towards the virtualization of IT services and infrastructure. When combined with SDN, it provides a complete solution for a fully virtualized network, offering unprecedented levels of agility and efficiency. The concept of Virtual Network Function (VNF) migration has introduced the need for optimized algorithms to minimize migration time and costs, addressing a critical aspect of resource utilization. This paper explores the utilization of machine learning methods, especially neural networks to enhance the migration of VNFs, and presents a framework leveraging Convolution Neural Networks (CNN) and Artificial Neural Networks (ANN) to predict optimal migration paths for VNFs, aiming to minimize migration time and cost. The proposed solution analyzes network conditions, workload patterns, and resource availability, enabling dynamic and efficient VNF re-Iocations. This approach significantly improves network performance and reliability, making it a vital contribution to the field of network function virtualization.