Traffic characteristics of on-line services

This paper presents a statistical characterization of on-line service traffic. The traffic is parameterized by the call holding times, the aggregate number of bytes and the number of packets transferred during the call. Particular attention is paid to modeling the stochastic rate function describing the call arrivals. The time of day and day of the week seasonal features produce long term correlations in the arrival process. In addition, short term correlations are found to exist within the hourly time scale. The arrival rates are in general nonstationary in the mean rate. This is attributed to the daily influx of new subscribers and other exogenous influences such as promotions and advertisements that trigger increased usage. A time series modeling methodology is used to identify and quantify these features. Under suitable transformations, a stationary rendition of the arrival process is derived and characterized by the short and long term correlation coefficients. The second order characterization of the arrival process is used to predict resource usage.