Peer-to-Peer Computing, IEEE International Conference on (2006)
Cambridge, United Kingdom
Sept. 6, 2006 to June 8, 2006
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/P2P.2006.30
Cong Shi , Shanghai Jiaotong University, China
Shicong Meng , Shanghai Jiaotong University, China
Yuanjie Liu , Shanghai Jiaotong University, China
Dingyi Han , Shanghai Jiaotong University, China
Yong Yu , Shanghai Jiaotong University, China
The idea of building query-oriented routing indices has changed the way of improving routing efficiency from the basis as it can learn the content distribution during the query routing process. It gradually improves routing efficiency with no excessive network overhead of the routing index construction and maintenance. However, the previously proposed mechanism is not practically effective due to the slow improvement of routing efficiency. In this paper, we propose a novel mechanism for queryoriented routing indices which quickly achieves high routing efficiency at low cost. The maintenance method employs reinforcement learning to utilize mass peer behaviors to construct and maintain routing indices. It explicitly uses the expected value of returned content number to depict the content distribution, which helps quickly approximate the real distribution. Meanwhile, the routing method is to retrieve as many contents as possible. It also helps speed up the learning process further. The experimental evaluation shows that the mechanism has high routing efficiency, quick learning ability and satisfactory performance under churn.
Y. Liu, C. Shi, Y. Yu, D. Han and S. Meng, "Reinforcement Learning for Query-Oriented Routing Indices in Unstructured Peer-to-Peer Networks," 2006 6th IEEE International Conference on Peer-to-Peer Computing(P2P), Cambridge, 2006, pp. 267-274.