Abstract
Rapid growth of network traffic causes the need for the development of new network technologies. Artificial intelligence provides suitable tools to improve currently used network optimization methods. In this paper, we propose a procedure for network traffic prediction. Based on optical networks’ (and other network technologies) characteristics, we focus on the prediction of fixed bitrate levels called traffic levels. We develop and evaluate two approaches based on different supervised machine learning (ML) methods—classification and regression. We examine four different ML models with various selected features. The tested datasets are based on real traffic patterns provided by the Seattle Internet Exchange Point (SIX). Obtained results are analyzed using a new quality metric, which allows researchers to find the best forecasting algorithm in terms of network resources usage and operational costs. Our research shows that regression provides better results than classification in case of all analyzed datasets. Additionally, the final choice of the most appropriate ML algorithm and model should depend on the network operator expectations.
Highlights
Quick and global development of network technologies such as the Internet of things, 5G, or cloud computing causes instant growth of endpoint devices
We propose four different machine learning (ML) models based on different selection of features: 1. window = 1,; 2. window = 10,; 3. window = 10, minute, day,; 4. window = 10, minute, day, traffic values from previous 24 h, 48 h, . . . , 336 h time intervals
In order to examine performance of the proposed ML methods in various situations that can occur in optical networks, for each analyzed dataset we created three different cases in terms of the number of traffic levels, namely, we considered 7, 12 and 20 traffic levels
Summary
Quick and global development of network technologies such as the Internet of things, 5G, or cloud computing causes instant growth of endpoint devices. Knowledge about future traffic in a network allows operators to optimize resource usage, provide better quality of service for users [12], reduce the cost of network operation, or detect anomalies in the traffic dataflow [13]. Knowledge obtained from each of the forecasting strategies can be used as valuable information for different network optimization tasks, e.g., traffic flow control, network operational cost reduction, anomalies detection, or physical network expansion. We focus on a short-term traffic prediction; our approach can be successfully applied to all forecasting types. We consider an optical network in which transmission between all physically connected nodes occurs continuously. ML algorithms locate, analyze, and learn those dependencies to forecast future network traffic flows.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
