Network Performance Measurement Based On Network Tomography Research, And Speculate

Today's Internet is a massive, distributed network which continues to explode in size as all kinds of traffic expanded rapidly. How to get the network performance on the existing infrastructure and manage network more reasonable or more effective is a challenge task. Especially, it is essential for the network managers and ISP. The heterogeneous and no-corporative structure of the Internet renders the tasks such as network behavior measurement extremely challenging.Many measurement tools require the cooperation with network nodes; they may affect the network real traffics and cause the security problems. For these reason, a promising technique named"Network Tomography"is emerging in recent years. It infers the network internal performance by end-to-end measurements, and makes a good performance in network fault detection.In this paper, we first introduce the recent research in network tomography, and then review the measurement and inference algorithm we could use Direct-MLE and EM-MLE. Because multicast protocols are not supported by significant portion of Internet and the routers treat differently between multicast packets and unicast packets. It is popular to use unicast back-to-back pairs as the probes to avoid those problems in multicast network. In this paper, we propose a new measurement based on TCP monitoring to get the packet pairs. The passive measurement does not influence the real network flows, so it is useful in heavy load network measurement.In this paper, we use the OPNET and Matlab model simulation to analyze the inference algorithms. We compare the difference on accuracy and convergence characteristic of direct inference algorithm and EM algorithm in active measurements. Simulation results show that EM algorithm has better convergence and can infer the internal performance effectively. We also analyze the searching space between two packet pairs which influences inference algorithm's accuracy. The conclusion shows that dynamic searching space is better than fixed searching space. At last, we introduce the delay inference in network tomography. We discuss the CGF model and MFMM methods in delay inference respectively. With Matlab simulation, it shows that EM algorithm in MFMM model is effectively.