S. P. Sone, J. J. Lehtomäki and Z. Khan, "Wireless Traffic Usage Forecasting Using Real Enterprise Network Data: Analysis and Methods," in IEEE Open Journal of the Communications Society, vol. 1, pp. 777-797, 2020, doi: 10.1109/OJCOMS.2020.3000059
Wireless traffic usage forecasting using real enterprise network data : analysis and methods
|Author:||Sone, Su P.1; Lehtomäki, Janne J.1; Khan, Zaheer1|
1Centre for Wireless Communications, University of Oulu, 90014 Oulu, Finland
|Online Access:||PDF Full Text (PDF, 3.6 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2020081460403
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2020-08-14
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.
IEEE open journal of the Communications Society
|Pages:||777 - 797|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work was supported in part by the Infotech Oulu through the framework of digital solutions in sensing and interactions, and in part by the Academy of Finland 6Genesis Flagship under Grant 318927.
|Academy of Finland Grant Number:||
318927 (Academy of Finland Funding decision)
© The Authors 2020. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.