OD Traffic Maps Recovery for Web 3.0 by Network Tomography in Hankel Tensor Space

In the emerging Web 3.0, origin-destination (OD) traffic maps play a crucial role in network maintenance and management. However, increasing network size and complexity, as well as insufficient or invalid NetFlow protocol-based measurements pose numerous challenges to recovering traffic maps for Web 3.0. This paper therefore proposes RNT-HTT, a robust Network Tomography model based on Hankel time-structured tensor, to accurately recover OD traffic maps with link loads and a fraction of NetFlow counts in Hankel tensor space. More specifically, we propose to Hankelize both OD traffic and link load matrices to three-way tensors along time direction, which fully exploits time-structured correlations concealed in network data. OD pairs-mode product is also designed to model the relation between the Hankelized OD traffic and link load tensors. On the basis of these, RNT-HTT formulates the recovery problem as a convex optimization program with tensor nuclear and ${{\ell }_{1}}$-norms to respectively effect traffic low-rank and noise sparsity characteristics. In addition, the block-iteration alternating direction method of multipliers (ADMM) and bidirectional pre-sampling schemes are developed to solve RNT-HTT reliably and efficiently. Extensive experiments on three real-world datasets verify effectiveness of RNT-HTT, and corroborate its superior performance over state-of-the-art methods in terms of the recovery accuracy.