  Network traffic demand matrix is a critical input for capacity planning,
anomaly detection and many other network management related tasks. The demand
matrix is often computed from link load measurements. The traffic matrix (TM)
estimation problem is the determination of the traffic demand matrix from link
load measurements. The relationship between the link loads and the traffic
matrix that generated the link load can be modeled as an under-determined
linear system and has multiple feasible solutions. Therefore, prior knowledge
of the traffic demand pattern has to be used in order to find a potentially
feasible demand matrix. In this paper, we consider the TM estimation problem
where we have information about the distribution of the demand sizes. This
information can be obtained from the analysis of a few traffic matrices
measured in the past or from operator experience. We develop an iterative
projection based algorithm for the solution of this problem. If large number of
past traffic matrices are accessible, we propose a Generative Adversarial
Network (GAN) based approach for solving the problem. We compare the strengths
of the two approaches and evaluate their performance for several networks using
varying amounts of past data.
