Predicción del consumo del ancho de banda de las aplicaciones web en la nube nativa basada en machine learning

Abstract

In this research, a comparative study of three neural network algorithms, which allow modeling a multilayer neural network, with a minimum of three layers ; selecting one, whose objective is to learn to predict the measurement of network traffic, which is connected to the cloud to validate the behavior of the network parameter of "bandwidth consumption", to optimize in time the network resources and ensure the improvement of the quality of service of web applications for small and medium enterprises. In recent years artificial neural networks have been used for predictive analysis, which outlines (Piedra et al., 2008), "Thus, some ANN models Artificial Neural Networks are used to determine projections from a data source; this feature can be exploited to make predictions, for example, to determine available bandwidth". Having an overview of the traffic flowing through the network allows to generate a network capacity planning when managing limited resources as in the case of small and medium enterprises, likewise (Piedra et al., 2008) justify the need to predict network traffic, "a traffic prediction system is required for planning and sizing purposes, this will allow to forecast traffic demands according to previous time periods".