@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} }