ISRL: intelligent search by reinforcement learning in unstructured peer-to-peer networks

Authors:
Xiuqi Li;Jie Wu;Shi Zhong
Affiliations:
Department of Computer Science and Engineering, Florida Atlantic University, Boca Raton, FL, USA;Department of Computer Science and Engineering, Florida Atlantic University, Boca Raton, FL, USA;SDS-Data Mining Research, Yahoo! Inc., Sunnyvale, CA, USA
Venue:
International Journal of Parallel, Emergent and Distributed Systems
Year:
2008

Citing 8
Cited 0

Prioritized Sweeping: Reinforcement Learning with Less Data and Less Time

Machine Learning
Understanding search engines: mathematical modeling and text retrieval

Understanding search engines: mathematical modeling and text retrieval
Swarm intelligence: from natural to artificial systems

Swarm intelligence: from natural to artificial systems
Machine Learning

Machine Learning
Location Awareness in Unstructured Peer-to-Peer Systems

IEEE Transactions on Parallel and Distributed Systems
Improving Unstructured Peer-to-Peer Systems by Adaptive Connection Establishment

IEEE Transactions on Computers
Handbook On Theoretical And Algorithmic Aspects Of Sensor, Ad Hoc Wireless, and Peer-to-Peer Networks

Handbook On Theoretical And Algorithmic Aspects Of Sensor, Ad Hoc Wireless, and Peer-to-Peer Networks
'Dominating-set-based' searching in peer-to-peer networks

International Journal of High Performance Computing and Networking

Quantified Score

Hi-index	0.00

Visualization

Abstract

Existing searches in unstructured peer-to-peer (P2P) networks are either blind or informed based on simple heuristics. Blind schemes suffer from low query quality. Simple heuristics lack theoretical background to support the simulation results. In this paper, we propose an intelligent searching scheme, called intelligent search by reinforcement learning (ISRL), which systematically seeks the best route to desired files by reinforcement learning (RL). In artificial intelligence, RL has been proven to be able to learn the best sequence of actions to achieve a certain goal. To discover the best path to desired files, ISRL not only explores new paths by forwarding queries to randomly chosen neighbors, but also exploits the paths that have been discovered for reducing the cumulative query cost. We design three models of ISRL: a basic version for finding one desired file, MP-ISRL for finding at least k files, and C-ISRL for reducing maintenance overhead through clustering when there are many queries. ISRL outperforms existing searching approaches in unstructured P2P networks by achieving similar query quality with lower cumulative query cost. The experimental result confirms the performance improvement of ISRL.