@article {Wei2014, title = {Rating Network Paths for Locality-Aware Overlay Construction and Routing}, journal = {IEEE/ACM Transactions on Networking}, volume = {23}, year = {2015}, month = {10/2015}, chapter = {1661}, abstract = {

This paper investigates the rating of network paths,\ i.e. acquiring quantized measures of path properties such as\ round-trip time and available bandwidth. Comparing to finegrained\ measurements, coarse-grained ratings are appealing in\ that they are not only informative but also cheap to obtain.

Motivated by this insight, we firstly address the scalable\ acquisition of path ratings by statistical inference. By observing\ similarities to recommender systems, we examine the applicability\ of solutions to recommender system and show that our\ inference problem can be solved by a class of matrix factorization\ techniques. A technical contribution is an active and progressive\ inference framework that not only improves the accuracy by\ selectively measuring more informative paths but also speeds\ up the convergence for available bandwidth by incorporating its\ measurement methodology.

Then, we investigate the usability of rating-based network\ measurement and inference in applications. A case study is\ performed on whether locality awareness can be achieved for\ overlay networks of Pastry and BitTorrent using inferred ratings.

We show that such coarse-grained knowledge can improve the\ performance of peer selection and that finer granularities do not\ always lead to larger improvements.

}, keywords = {matrix factorization, network inference, rating-based network measurement, recommender system}, author = {Du, Wei and Liao, Yongjun and Tao, Narisu and Geurts, Pierre and Fu, Xiaoming and Leduc, Guy} } @article {Lia2013, title = {DMFSGD: A decentralized matrix factorization algorithm for network distance prediction}, journal = {IEEE/ACM Transactions on Networking}, volume = {21}, year = {2013}, month = {10/2013}, chapter = {1511}, abstract = {

The knowledge of end-to-end network distances is\ essential to many Internet applications. As active probing of all\ pairwise distances is infeasible in large-scale networks, a natural\ idea is to measure a few pairs and to predict the other ones\ without actually measuring them. This paper formulates the\ prediction problem as matrix completion where the unknown\ entries in a pairwise distance matrix constructed from a network\ are to be predicted. By assuming that the distance matrix has\ a low-rank characteristics, the problem is solvable by lowrank\ approximation based on matrix factorization. The new\ formulation circumvents the well-known drawbacks of existing\ approaches based on Euclidean embedding.

A new algorithm, so-called Decentralized Matrix Factorization\ by Stochastic Gradient Descent (DMFSGD), is proposed. By\ letting network nodes exchange messages with each other, the\ algorithm is fully decentralized and only requires each node\ to collect and to process local measurements, with neither\ explicit matrix constructions nor special nodes such as landmarks\ and central servers. In addition, we compared comprehensively\ matrix factorization and Euclidean embedding to demonstrate\ the suitability of the former on network distance prediction. We\ further studied the incorporation of a robust loss function and\ of non-negativity constraints. Extensive experiments on various\ publicly-available datasets of network delays show not only the\ scalability and the accuracy of our approach, but also its usability\ in real Internet applications.

}, keywords = {matrix completion, matrix factorization, network distance prediction, stochastic gradient descent}, author = {Yongjun Liao and Wei Du and Pierre Geurts and Guy Leduc} }