Fuzzy inference system for Internet traffic load forecasting

Conventional statistical analysis of Internet traffic data is often employed to determine traffic distribution, to summarize users behavior patterns, or to predict future network traffic for network management and planning. However, the statistical techniques like autoregressive integrated moving average (ARIMA) models fail to capture some peculiar network traffic characteristics like Self-similarity, Long-range dependency (LRD), etc. With rapid growth of Internet, accurate and reliable forecasting is essential for the network resource management. Therefore, the present work explores to utilize computational intelligence techniques like fuzzy logic to predict Internet traffic data pattern. This paper presents the development of Fuzzy inference system (FIS) for one benchmark dataset and one real dataset. The experimental results of the FIS of the benchmark dataset Mackey-Glass series and real dataset JNU LAN traffic data collected for one hour are presented. The performance of the FIS is estimated using cross-validation method by measuring root mean square error (RMSE).