Adaptive sampling for network performance measurement under voice traffic

As the Internet grows in scale and complexity, the benefits of network performance measurements and monitoring are significantly increasing. Sampling-based measurement methods provide adequate techniques for reducing the quantity of control data and attract growing interests. The research reported in this paper, addresses the issue of how to carry out the sampling in an adaptive fashion, so that the accuracy for measuring the quality of service parameters (delay, loss, jitter, throughput) is better if we know something about the traffic type and traffic parameters. Our study proposes and investigates a mechanism that can be set up to adaptively adjust the parameters of the sampling technique. Two realistic network topologies based on MPLS networks are setup to evaluate the proposed adaptive sampling scheme for monitoring and measuring network performance metrics. Compared with conventional sampling techniques (systematic and stratified sampling), simulation results are presented to illustrate that adaptive sampling provides the potential for better monitoring, control, and management of high-performance networks with higher accuracy.