Rating Network Paths for Locality-Aware Overlay Construction and

Rating Network Paths for Locality-Aware Overlay Construction and
This paper investigates the rating of network paths, i.e., acquiring
quantized measures of path properties such as round-trip time and
available bandwidth. Compared to fine-grained measurements, coarsegrained ratings are appealing in that they are not only informative but also
cheap to obtain. Motivated by this insight, we first address the scalable
acquisition of path ratings by statistical inference. By observing similarities
to recommender systems, we examine the applicability of solutions to a
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 ratingbased 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