Better Coarse-Grained than Fine-Grained: Rating Network Paths for Locality-Aware Overlay Construction and Routing
Wei Du, University of Goettingen, Germany.
Date and Time
Tuesday, September 10th, 2013 at 13:30.
Polacksbacken, room 1345
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 fine-grained 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.
About the speaker
Wei Du is a postdoctoral researcher at the networking group in University of Göttingen, Germany. He received his B.S. in 1997 from Tianjin University, China, and PhD in 2002 from Institute of Computing Technology, Chinese Academy of Sciences, China. Since graduation, he has been working as postdoctoral researcher at INRIA, France, Hamburg University, Germany, University of Liège, Belgium, and University of Innsbruck, Austria. His main research interests are computer networking and machine learning.