Traffic data analysis based on extreme value theory and its applications

It is important to predict serious deterioration of telecommunication quality. The purpose of this paper is to predict such serious events by analyzing only a "short" period of teletraffic data. It presents a method for analyzing the tail distributions (TD) of variables concerning teletraffic states, because TD are suitable to represent serious events. This method is based on extreme value theory (EVT), which provides a firm theoretical foundation for the analysis. To be more precise, we use throughput data measured on an actual network in daily busy hours for 15 min, and use its first 10 s (known data) to analyze the TD. Then, we evaluate how well the obtained TD can predict the TD of the remaining 890 s (unknown data). The result shows that the obtained TD, based on EVT by analyzing the small amount of known data, can predict the TD of the unknown data much better than methods based on an empirical distribution and log-normal distribution. Furthermore, we apply the obtained TD to predict the peak throughput in unknown data. The results of this paper enable us to predict serious events with lower measurement cost.