Freenet: a distributed anonymous information storage and retrieval system
International workshop on Designing privacy enhancing technologies: design issues in anonymity and unobservability
Heuristically Optimized Trade-Offs: A New Paradigm for Power Laws in the Internet
ICALP '02 Proceedings of the 29th International Colloquium on Automata, Languages and Programming
Adaptive Probabilistic Search for Peer-to-Peer Networks
P2P '03 Proceedings of the 3rd International Conference on Peer-to-Peer Computing
Analysis and comparison of P2P search methods
InfoScale '06 Proceedings of the 1st international conference on Scalable information systems
Modeling message propagation in random graph networks
Computer Communications
Dynamic TTL-Based Search in Unstructured Peer-to-Peer Networks
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
| Hi-index | 0.00 |
Random Walks (RW) search technique can greatly reduce bandwidth production but generally fails to adapt to different workloads and environments. A Random Walker can’t learn anything from its previous successes or failures, displaying low success rates and high latency. In this paper, we propose Intelligent Walks (IW) search mechanism – a modification of RW, exploiting the learning ability and the shortest path distance of node neighbors. A node probes its neighbors before forwarding the query. The probe is to find a candidate that has the shortest distance from the query source and/or has ever seen before the object that is going to be sent. If there isn’t such candidate, then a node is chosen as usual (at random). The experimental results demonstrate that new method achieves better performance than RW in terms of success rate.