Analysis of Dynamic Distribution Methods of Telecommunication Services Based on Virtual Network Function
Abstract
This analytical article studies the methods of dynamic allocation of Virtual Network Functions (VNFs), which are the main component of Network Function Virtualization (NFV) technology. The sharp increase in the volume of telecommunication services and the complexity of their quality requirements require a transition from traditional static resource allocation models to dynamic, flexible and intelligent approaches. The article analyzes the main problems of dynamic allocation of VNFs - the processes of placement, scaling and resource reallocation. It also presents a comparative analysis of reactive, proactive and hybrid approaches used to solve these problems, including algorithms based on optimization, heuristics and artificial intelligence. The research results show that approaches based on reinforcement learning and predictive models can increase resource utilization efficiency by up to 30% compared to traditional methods and guarantee the quality of service.