Title :
Adaptive Decentralized Routing in Small-World Networks
Author :
Bakun, Oleg ; Konjevod, Goran
Author_Institution :
Arizona State Univ., Tempe, AZ, USA
Abstract :
We study routing in a small-world network modeled by an augmented grid, where Kleinberg´s expected polylog bound offers a theoretical explanation for the empirically observed efficiency of greedy routing. We improve upon greedy through decentralized machine learning, using (local) ensembles of experts to choose routing edges and learn from feedback. In experiments with both synthetic and real networks, we observe improvements over greedy routing even when nodes are blind (do not know their neighbors´ locations) or a fraction of nodes are uncooperative.
Keywords :
greedy algorithms; learning (artificial intelligence); telecommunication computing; telecommunication network routing; Kleinberg expected polylog bound; adaptive decentralized routing; augmented grid; decentralized machine learning; greedy routing; local ensembles; small-world networks; Artificial intelligence; Communications Society; Costs; Feedback; Learning systems; Machine learning; Network topology; Peer to peer computing; Routing; Solid modeling;
Conference_Titel :
INFOCOM IEEE Conference on Computer Communications Workshops , 2010
Conference_Location :
San Diego, CA
Print_ISBN :
978-1-4244-6739-6
Electronic_ISBN :
978-1-4244-6739-6
DOI :
10.1109/INFCOMW.2010.5466704
