Constraint Network Tomography And Its Active Network Simulation To Achieve

The knowledge of network parameters allows network engineers to improve the network design, control and operate. It is hard to get the important parameters such as link delay and loss rate directly, because the Internet has becomes massive, distributed and heterogeneous.Network Tomography is now a hotspot for inference the internal link delay and loss rate with the end-to-end measurement data .We call current network tomography as unconstraint network tomography for its not adding any constraint condition and all of them nearly are based on some likelihood algorithm. Under some perfect and hypothetical condition, it works well. Unfortunately, the real situation is complicated and there is some difference between the actual end-to-end measurement data and the ideal ones. Furthermore, it makes the evaluated results fluctuating badly. In this paper, we introduce a new constraint Network Tomography to solve this problem, which adds some constraint information collecting with the help of some partial internal nodes to the model. In fact, the constraint network tomography can improve the precision of evaluated parameters better than the other method.Especially, both the constraint link delay inference and constraint link loss rate inference are investigated. In chapter 2 and 3, we introduce how to build the mathematical model for these problems and the steps to the estimated final results .Besides this, in chapter 5, how to simulate the algorithms using OPNET tools is also discussed.Comparing with the results, it proves that applying constraint optimization method to Network Tomography is very effective.Active network is regard as the most potential technology in the future, and it can be customized to compute the packet flow. In the active network circumstance, it is convenient to collect the measurement data and constraint information, and organize the measurement task because of its programmable and cooperative capablility. By distribute the computing task to the cooperative nodes, it can decrease the network additional load heavily in terms of bandwidth and resources and makes network tomography easy to apply. Finally, we demonstrate its performance using extensive...