Peer-to-peer architecture (Computer networks)

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.

Efficient searching is one of the important design issues in peer-to-peer (P2P) networks. Among various searching techniques, semantic-based searching has drawn significant attention recently. Gnutella-like efficient searching system (GES) [29] is such a system. GES derives node vector , a semantic summary of all documents on a node based on vector space model (VSM). The node-based topology adaptation algorithm and search protocol are then discussed. However, when there are many categories of documents at each node, the node vector representation may be inaccurate. We extend the idea of GES and present a class-based search system (CSS). It makes use of a document clustering algorithm: OSKM [27] to cluster all documents on a node into several classes. Each class can be viewed as a virtual node. As a result, class vector replaces node vector and plays an important role in class-based topology adaptation and search process, which makes CSS very efficient. Our simulation demonstrates that CSS outperforms GES.

Peer-to-Peer (P2P) systems are characterized by direct access between peer computers, rather than through a centralized server. File sharing is the dominant P2P application on the Internet, allowing users to easily contribute, search and obtain content. The objective of this thesis was to design XYZ, a partially centralized, scalable and self-organizing lookup service for wide area P2P systems. The XYZ system is based on distributed hash table (DHT). A unique ID and a color assigned to each node and each file. The author uses clustering method to create the system backbone by connecting the cluster heads together and uses color clustering method to create color overlays. Any lookup for a file with a color will only be forwarded in the color overlay with the same color so that the searching space is minimized. Simulations and analysis are also provided in this thesis.