Browsing by Author "Palavalli, S.R."

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    Search improvement in unstructured P2P network considering type of content
    (2008) Totekar, C.R.; Vani, M.; Palavalli, S.R.
    One of the key challenging aspects of peer-to-peer systems has been efficient search for objects. To achieve this, we need to minimize the number of nodes that have to be searched, thereby use minimum number of messages during the search process. This can be done by selectively sending requests to nodes having higher probability of a hit for the queried object. In this paper we present an algorithm CBWS, for searching in unstructured peer-to-peer network, which is based on the fact that most users in peer-to-peer network share various types of data(e.g. audio, video, text, archive) in different proportions. The information about the number of objects of each file-type shared by nodes, is used to selectively forward the query to a node having higher hit-ratio for the data objects of requested type, based on the history of recently succeeded queries. Simulation results prove that our searching algorithm performs better than the selective walk searching algorithm.
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    Search improvement in unstructured P2P network considering type of content
    (2008) Totekar, C.R.; Vani, M.; Palavalli, S.R.
    One of the key challenging aspects of peer-to-peer systems has been efficient search for objects. To achieve this, we need to minimize the number of nodes that have to be searched, thereby use minimum number of messages during the search process. This can be done by selectively sending requests to nodes having higher probability of a hit for the queried object. In this paper we present an algorithm CBWS, for searching in unstructured peer-to-peer network, which is based on the fact that most users in peer-to-peer network share various types of data(e.g. audio, video, text, archive) in different proportions. The information about the number of objects of each file-type shared by nodes, is used to selectively forward the query to a node having higher hit-ratio for the data objects of requested type, based on the history of recently succeeded queries. Simulation results prove that our searching algorithm performs better than the selective walk searching algorithm.