Forecasting Network Traffic at Large Time Scales by Using Dual-Related Method

Liangrui TANG  Shiyu JI  Shimo DU  Yun REN  Runze WU  Xin WU  

IEICE TRANSACTIONS on Communications   Vol.E100-B   No.11   pp.2049-2059
Publication Date: 2017/11/01
Online ISSN: 1745-1345
DOI: 10.1587/transcom.2017EBP3014
Type of Manuscript: PAPER
Category: Network
large time scales,  network traffic prediction,  dual-related method,  correlation analysis,  

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Network traffic forecasts, as it is well known, can be useful for network resource optimization. In order to minimize the forecast error by maximizing information utilization with low complexity, this paper concerns the difference of traffic trends at large time scales and fits a dual-related model to predict it. First, by analyzing traffic trends based on user behavior, we find both hour-to-hour and day-to-day patterns, which means that models based on either of the single trends are unable to offer precise predictions. Then, a prediction method with the consideration of both daily and hourly traffic patterns, called the dual-related forecasting method, is proposed. Finally, the correlation for traffic data is analyzed based on model parameters. Simulation results demonstrate the proposed model is more effective in reducing forecasting error than other models.