Traffic data analysis based on extreme value theory and its applications

It is important to predict serious deterioration of the telecommunication quality. The purpose of this paper is to predict such serious events by analyzing only a "short" period (i.e., a "small" amount) of teletraffic data. To achieve this end, this paper presents a method for analyzing the tail distributions of variables concerning teletraffic states, because tail distributions 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, in this paper, we use throughput data measured on an actual network in daily busy hours for 15 minutes, and use its first 10 seconds (known data) to analyze the tail distribution. Then, we evaluate how well the obtained tail distribution can predict the tail distribution of the remaining 890 seconds (unknown data). The result shows that the obtained tail distribution based on EVT by analyzing the small amount of known data can predict the tail distribution of the unknown data much better than methods based on an empirical distribution and log-normal distribution. Furthermore, we apply the obtained tail distribution to predict the peak throughput in unknown data. The results of this paper enable us to predict serious events with lower measurement cost.