Abstract
Wireless traffic usage forecasting methods can help to facilitate proactive resource allocation solutions in cloud managed wireless networks. In this paper, we present temporal and spatial analysis of network traffic using real traffic data of an enterprise network comprising 470 access points (APs). We classify and separate APs into different groups according to their traffic usage patterns. We study various statistical properties of traffic data, such as auto-correlations and cross-correlations within and across different groups of APs. Our analysis shows that the group of APs with high traffic utilization have strong seasonality patterns. However, there are also APs with no such seasonal patterns. We also study the relation between number of connected users and traffic generated, and show that more connected users do not always mean more traffic data, and vice versa. We use Holt-Winters, seasonal auto-regressive integrated moving average (SARIMA), long short-term memory (LSTM), gated recurrent unit (GRU) and convolutional neural network (CNN) methods for forecasting traffic usage. Our results show that there is no single universal best method that can forecast traffic usage of every AP in an enterprise wireless network. The combined models such as CNN-LSTM and CNN-GRU are also used for spatio-temporal forecasting of a single AP traffic usage. The results show that considering spatial dependencies of neighboring APs can improve the forecasting performance of a single AP if it has significant spatial correlations.
Highlights
D IFFERENT from the previous generation of wireless networks, fifth generation (5G) and beyond wireless networks are expected to provide wireless connectivity to billions of devices and they would be required to have improvements in data speed and to incorporate support for ultra-reliable low latency communication (URLLC)
The main contributions of this paper are: 1) To perform traffic usage forecasting that can be used at a resource controller of an enterprise network for proactive resource allocation, we study temporal and spatial dependencies of traffic usage data collected over a period of more than a month from 470 access points (APs) deployed in the University of Oulu
REASONS OF CHOOSING AND COMPARING THE PRESENTED FORECASTING METHODS As wireless traffic time series often exhibit seasonal patterns, one classical approach that is available for forecasting such data is Holt-Winters which is known as triple exponential smoothing method
Summary
D IFFERENT from the previous generation of wireless networks, fifth generation (5G) and beyond wireless networks are expected to provide wireless connectivity to billions of devices and they would be required to have improvements in data speed and to incorporate support for ultra-reliable low latency communication (URLLC). To the best of our knowledge, this is the first time both temporal and spatial analysis for forecasting wireless traffic data of a real enterprise network is performed and the famous statistical methods and state-of-the-art machine learning methods are compared. The main contributions of this paper are: 1) To perform traffic usage forecasting that can be used at a resource controller of an enterprise network for proactive resource allocation, we study temporal and spatial dependencies of traffic usage data collected over a period of more than a month from 470 APs deployed in the University of Oulu.
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