Model
Digital Document
Publisher
Florida Atlantic University
Description
Peer-to-peer (P2P) networking has been receiving increasing attention from the
research community recently. How to conduct efficient and effective searching in such
networks has been a challenging research topic. This dissertation focuses on unstructured
file-sharing peer-to-peer networks. Three novel searching schemes are proposed,
implemented, and evaluated. In the first scheme named ISRL (Intelligent Search by Reinforcement
Learning), we propose to systematically learn the best route to desired files
through reinforcement learning when topology adaptation is impossible or infeasible. 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. Three models of ISRL are put forwarded: a
basic version for finding one desired file, MP-ISRL (MP stands for Multiple-Path ISRL)
for finding at least k files, and C-ISRL (C refers to Clustering) for reducing maintenance
overhead through clustering when there are many queries. ISRL outperforms existing searching approaches in unstructured peer-to-peer networks by achieving similar query
quality with lower cumulative query cost. The experimental results confirm the performance
improvement of ISRL. The second approach, HS-SDBF (Hint-based Searching
by Scope Decay Bloom Filter), addresses the issue of effective and efficient hint propagation.
We design a new data structure called SDBF (Scope Decay Bloom Filter) to
represent and advertise probabilistic hints. Compared to existing proactive schemes, HSSDBF
can answer many more queries successfully at a lower amortized cost considering
both the query traffic and hint propagation traffic. Both the analytic and the experimental
results support the performance improvement of our protocol. The third algorithm, hybrid
search, seeks to combine the benefits of both forwarding and non-forwarding searching
schemes. In this approach, a querying source directly probes its own extended neighbors
and forwards a query to a subset of its extended neighbors and guides these neighbors
to probe their own extended neighbors on its behalf. The hybrid search is able to adapt
query execution to the popularity of desired files without generating too much state maintenance
overhead because of the 1-hop forwarding inherent in the approach. It achieves
a higher query efficiency than the forwarding scheme and a better success rate than the
non-forwarding approach. To the best of our knowledge, this work is the first attempt
to integrate forwarding and non-forwarding schemes. Simulation results demonstrate the
effectiveness of the hybrid search.
research community recently. How to conduct efficient and effective searching in such
networks has been a challenging research topic. This dissertation focuses on unstructured
file-sharing peer-to-peer networks. Three novel searching schemes are proposed,
implemented, and evaluated. In the first scheme named ISRL (Intelligent Search by Reinforcement
Learning), we propose to systematically learn the best route to desired files
through reinforcement learning when topology adaptation is impossible or infeasible. 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. Three models of ISRL are put forwarded: a
basic version for finding one desired file, MP-ISRL (MP stands for Multiple-Path ISRL)
for finding at least k files, and C-ISRL (C refers to Clustering) for reducing maintenance
overhead through clustering when there are many queries. ISRL outperforms existing searching approaches in unstructured peer-to-peer networks by achieving similar query
quality with lower cumulative query cost. The experimental results confirm the performance
improvement of ISRL. The second approach, HS-SDBF (Hint-based Searching
by Scope Decay Bloom Filter), addresses the issue of effective and efficient hint propagation.
We design a new data structure called SDBF (Scope Decay Bloom Filter) to
represent and advertise probabilistic hints. Compared to existing proactive schemes, HSSDBF
can answer many more queries successfully at a lower amortized cost considering
both the query traffic and hint propagation traffic. Both the analytic and the experimental
results support the performance improvement of our protocol. The third algorithm, hybrid
search, seeks to combine the benefits of both forwarding and non-forwarding searching
schemes. In this approach, a querying source directly probes its own extended neighbors
and forwards a query to a subset of its extended neighbors and guides these neighbors
to probe their own extended neighbors on its behalf. The hybrid search is able to adapt
query execution to the popularity of desired files without generating too much state maintenance
overhead because of the 1-hop forwarding inherent in the approach. It achieves
a higher query efficiency than the forwarding scheme and a better success rate than the
non-forwarding approach. To the best of our knowledge, this work is the first attempt
to integrate forwarding and non-forwarding schemes. Simulation results demonstrate the
effectiveness of the hybrid search.
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