Evaluation of adaptive statistical sampling versus random sampling for video traffic

The growth in real-time applications transmitted over computer networks means that the quality of service (QoS) parameters of these applications need to be assessed and quantified in order for critical real-time applications such as videoconferencing to be delivered with an appropriate level of quality. However, most real-time applications generate a large amount of traffic data. The process of measuring QoS parameters for these data is not practically feasible in real-time. Therefore, in order to reduce the data processing and storage, sampling is an essential operation. In fixed rate sampling the number of data packets processed remains unchanged even when the traffic characteristics change. In adaptive sampling the number of packets sampled varies in accordance with traffic rate. This makes the processing more efficient. In this paper, a comparison of adaptive statistical sampling approach versus random sampling was carried out. The adaptive statistical sampling method adjusts the sampling rate by determining the statistical variations of packet arrival rate. A suitable network was simulated using ns-2 package to carry out this investigation. The study demonstrated that the performance of adaptive statistical sampling was better than random sampling.