Abstract
Designing efficient search algorithms is a key challenge in unstructured peer-to-peer networks. Flooding and random walk (RW) are two typical search algorithms. Flooding searches aggressively and covers the most nodes. However, it generates a large amount of query messages and, thus, does not scale. On the contrary, RW searches conservatively. It only generates a fixed amount of query messages at each hop but would take longer search time. We propose the dynamic search (DS) algorithm, which is a generalization of flooding and RW. DS takes advantage of various contexts under which each previous search algorithm performs well. It resembles flooding for short-term search and RW for long-term search. Moreover, DS could be further combined with knowledge-based search mechanisms to improve the search performance. We analyze the performance of DS based on some performance metrics including the success rate, search time, query hits, query messages, query efficiency, and search efficiency. Numerical results show that DS provides a good tradeoff between search performance and cost. On average, DS performs about 25 times better than flooding and 58 times better than RW in power-law graphs, and about 186 times better than flooding and 120 times better than RW in bimodal topologies.
Designing efficient search algorithms is a key challenge in unstructured peer-to-peer networks. Flooding and random walk (RW) are two typical search algorithms. Flooding searches aggressively and covers the most nodes. However, it generates a large amount of query messages and, thus, does not scale. On the contrary, RW searches conservatively. It only generates a fixed amount of query messages at each hop but would take longer search time. We propose the dynamic search (DS) algorithm, which is a generalization of flooding and RW. DS takes advantage of various contexts under which each previous search algorithm performs well. It resembles flooding for short-term search and RW for long-term search. Moreover, DS could be further combined with knowledge-based search mechanisms to improve the search performance. We analyze the performance of DS based on some performance metrics including the success rate, search time, query hits, query messages, query efficiency, and search efficiency. Numerical results show that DS provides a good tradeoff between search performance and cost. On average, DS performs about 25 times better than flooding and 58 times better than RW in power-law graphs, and about 186 times better than flooding and 120 times better than RW in bimodal topologies.
Existing Networks:
P2P
Networks establish loosely coupled application level overlays on top of the
internet to facilitate efficient sharing of resources .Having less constraints
over network topology, Unstructed P2P networks can be constructed very
efficiently and are therefore considered suitable to the internet environment.
Random search strategies adopted by unstructured P2P networks usually perform
poorly with the large network size. Search performance offered by the existing
technique is not optimal as desired
Proposed System:
To
enhance the search performance in unstructured P2P network is used to exploit
the User interest pattern. A search protocol and a routing table updating
protocol are further proposed in order to expedite the search process through
self organizing P2P network. In addition to this a protocol has been proposed
to drive the distributed search queried files through peer’s local interactions.
So the search performance offered is Optimal. High degree of optimality can be
achieved using this technique. Improved performance of search offered using
network protocols in an efficient manner.
Requirement Analysis:
Software Requirements
Java1.5
Java Swing
Sql Server 2000
Windows Xp.
Hardware Requirements
Hard
disk : 60GB
RAM
: 1GB
Processor : P IV
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