Analysis of on-off patterns in VoIP and their effect on voice traffic aggregation

We present an experimental analysis of on-off patterns in voice over IP (VoIP), where we study the talk-spurt/gap distribution produced by two modern silence detectors: ITU G.729 Annex B Voice Activity Detector (VAD) and NeVoT Silence Detector (SD). The results indicate that spurt/gap distributions are fairly sensitive to both the sound volume and the type of silence detectors, but all showed that the traditional assumption of exponential distribution does not always fit well with the audio sessions we recorded. Both the spurt and gap distributions are more “heavy-tailed” than the exponential curve. In particular, the gap distribution deviates much more strongly from the exponential model, even when “hangover” is applied. To estimate how such deviation affects VoIP applications, we investigate the performance of voice traffic multiplexing. In particular, we look at the probability of having a out-of-profile packet (p_o) when a token bucket filter is placed at the multiplexing end. We ran a series of simulations under three increasingly accurate settings: the exponential model, the real CDF, and the raw silence detector outputs. The token bucket results are robust with regard to the details of the distribution. This is particularly true when the multiplexing factor N is large and the token buffer size B is not too big. When N is small and/or B is big, however, the estimated p_o under the real CDF is about 30% to 200% larger than under the exponential model. The relative difference between the raw silence detector outputs and the real CDF is generally much smaller than between the real CDF and the exponential model. Therefore, the data traffic in VoIP has a small temporal correlation and a secondary effect on the multiplexer performance