Statistical Estimation in Network Tomography

• Activity Number: 346
• Type: Invited
• Date/Time: Wednesday, August 10, 2005 : 8:30 AM to 10:20 AM
• Sponsor: Section on Physical and Engineering Sciences
• Author(s): Gang Liang*+ and Bin Yu
• Companies: University of California, Irvine and University of California, Berkeley
• Keywords: network tomography ; pseudo likelihood ; traffic matrix ; multicast

Abstract

Network monitoring and diagnosis are key to improving network performance. The difficulties of performance monitoring lie in today's fast growing internet, accompanied by increasingly eterogeneous and unregulated structures. Moreover, these tasks become even harder because one cannot rely on the collaboration of individual routers and servers to directly measure network traffic. Even though the aggregative nature of possible network measurements gives rise to inverse problems, existing methods for solving inverse problems usually are computationally intractable or statistically inefficient. In this talk, we will discuss a pseudo-likelihood approach for solving a group of network tomography problems. The approach uses the principle of divide-and-conquer to achieve a good balance between the computational complexity and the statistical efficiency of the parameter estimation. Under general regularity conditions, the consistency and asymptotic normality of the pseudo-likelihood estimator are established.