Adaptive sampling methods for network performance metrics measurement and evaluation in MPLS-based IP networks.

摘要

As the Internet grows in scale and complexity, the benefits of network performance measurements and monitoring are significantly increasing. Besides having a good knowledge about the behavior of the network for operational purposes, measurements can assist the traffic engineering algorithms in the optimization and dimensioning of the network by providing the feedback to operators or management systems in an efficient fashion. In selecting a network measurement and monitoring method there is a fundamental design trade-off, between overhead resource costs, and the accuracy and timeliness of the data. In order to plan a suitable sampling strategy it is therefore crucial to determine what information is needed and the desired degree of accuracy in advance. Sampling-based measurement methods provide adequate techniques for reducing the quantity of control data and attract growing interests.; The research reported in this dissertation, 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 investigates the mechanisms that can be set up to adaptively adjust the parameters of the sampling technique. Two realistic network topologies based on an MPLS-based IP networks are used to evaluate the adaptive sampling schemes for monitoring and measuring network performance metrics in relevant to voice, video and Internet data applications. 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.