ТЕОРЕТИЧЕСКАЯ ОЦЕНКА ИСПОЛЬЗОВАНИЯ МАТЕМАТИЧЕСКИХ МЕТОДОВ ПРОГНОЗИРОВАНИЯ ЗАГРУЗКИ ВИРТУАЛЬНОЙ ИНФРАСТРУКТУРЫ

Abstract

Introduction: Nowadays virtualization is used everywhere, and it is especially necessary for creating the network and server infrastructure of the enterprise. Without mathematical forecasting, it is impossible to accurately predict the behavior of the infrastructure over a time period. Consequently, the task was set – to create the most accurate forecast for the longest possible period. Methods: To create the forecast, an Autoregressive Integrated Moving Average model based on time series was chosen. This model has several different modifications, such as a stately model, an extended model, a seasonal model. The description of the Autoregressive Integrated Moving Average mathematical models, as well as the extended model of the Autoregressive Integrated Moving Average and the seasonal model of the Autoregressive Integrated Moving Average, are considered. Based on the mathematical description, the forecasting methods were studied and compared. Then the methods of mathematical forecasting were implemented in software, graphs were built, a comparison was made, and the best of them was identified to predict the behavior of the network infrastructure. The tests showed that the Autoregressive Integrated Moving Average model predicts the behavior of the network infrastructure for a week, the extended Autoregressive Integrated Moving Average model predicts the behavior of the network infrastructure for a month, and the seasonal Autoregressive Integrated Moving Average model predicts the behavior of the network infrastructure for a year. Practical significance: The results obtained based on the results of the Box-Jenkins mathematical forecasting modeling have wide practical application for monitoring a load of virtual infrastructure elements in order to prevent system failures and track anomalies, which increases the efficiency of resource use and infrastructure security.