Networks virtualization as an approach to optimization of computer networks

Abstract

The purpose of the work is to study the existing methods of optimizing computer networks and analyze the approach of virtualization of networks as a means of optimization. The object of the work is the process of optimizing computer networks, and the subject is models, methods and information technologies that are used to optimize networks.

Research methods: simulation and mathematical modeling methods, optimization methods, control methods, neural network methods.

As a result of the work, an analysis of computer network optimization approaches and methods was carried out. Among them, optimization methods of network topology, methods of nonlinear optimization of parameters and functional dependencies, which describe the behavior and state of the network, are highlighted. It is noted that the implementation of machine learning methods in the optimization model of computer networks is promising due to their ability to generalize, classify and predict possible changes in the network structure to improve its efficiency. The focus is on a virtualization approach that allows you to abstract from network topology, optimize resource usage, improve security, simplify management, and ensure high availability. Such models can be adapted to specific requirements and constraints. Among the existing directions of virtualization, the virtualization of network functions, the construction of software-configured networks and knowledge-defined networks are considered in detail.

Conclusions: it is proposed to combine the virtualization approach with machine learning methods, namely to build a knowledge-based network optimization model based on graph neural networks. This approach will make it possible to combine the complex relationship between topology, routing and incoming network traffic and obtain accurate estimates of the distribution of delays and losses in the network.