Hewlett-Packard Development Company, L.p.

Vasiliki Kalogeraki

James Christopher Pruyne

Murali Krishna Ramanathan

A service search network system includes a plurality of file search nodes. Each file search node has a file repository that stores files searchable by a file search request. A first and a second service search node is provided, each having a service repository for storing services that can be searched. When the first service search node receives a service search request for a particular service stored in the second service search node, the first service search node formats the service search request into a format recognized by the file search nodes such that the service request can be propagated to the second service search node via some of the file search nodes. The structure of each service search node is also described. A method of searching a specific service in a search network having file search nodes and service search nodes is also described.

Search network for searching services on the internet

Peers (p) monitor responses received from other peers (s, q) to requests for information and rate the responding peers on their ability to provide information of interest. When a responding peer (s) is discovered to frequently provide good results, the requesting peer (p) attempts to move closer to the responding peer (s) by creating a direct connection (p-s) with that peer and thereby promote the 'good' peer to an 'immediate' peer. If such a promotion would result in too many direct connections, the least important immediate peer is demoted to an &#x201c;indirect&#x201d; peer. The criteria (Imp) used for evaluating the relative importance of at least the immediate peers is preferably a time weighted average (Imp*(t)=&#x3b1;&#xb7;Imp(t)+&#x3b2;&#xb7;Imp*(t&#x2212;1)) that also measures consistency and reliability and preferably includes factors not only representative of the peer's ability to provide requested information (Hits), but also of its proximity to the source of that information (1/Hops) so that it can prove that information efficiently. This leads to clusters of peers with similar interests, and in turn reduces the depth of searches typically required to achieve good results.

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Sunnyvale, CA, United States of America

Vasiliki Kalogeraki