Advances in Grid and Pervasive Computing: Second by Christophe Cérin, Kuan-Ching Li

By Christophe Cérin, Kuan-Ching Li

This e-book constitutes the refereed complaints of the second one overseas convention on Grid and Pervasive Computing, GPC 2007, held in Paris, France in may perhaps 2007.

The fifty six revised complete papers and 12 revised brief papers have been rigorously chosen from 217 submissions in the course of rounds of reviewing and development. The papers handle all points of grid and pervasive computing and concentrate on issues reminiscent of cluster computing, excessive functionality computing, grid computing, semantic internet and semantic grid, service-oriented computing, peer-to-peer computing, pervasive computing, cellular computing, community garage, in addition to grid and pervasive comparable applications.

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Extra info for Advances in Grid and Pervasive Computing: Second International Conference, GPC 2007, Paris, France, May 2-4, 2007, Proceedings

Sample text

Thus, they may be represented in different vocabularies, making difficult their shared exploitation. In this work, we assume that the participants to a collaborative database application agree on a Common Background Knowledge (CBK) that will be used locally by each summarization process. An example of such a CBK is the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) [10], which is a comprehensive clinical terminology covering diseases, clinical findings, and procedures. g. [19], [2]).

We validated our algorithmic solutions through simulation, using the BRITE topology generator and SimJava. The performance results show that the cost of query routing is minimized, while incurring a low cost of summary maintenance. The rest of this paper is organized as follows. Section 2 describes PeerSum’s summary model. Section 3 describes PeerSum’s summary management with its algorithms. Section 4 discusses query processing with PeerSum. Section 5 gives a performance evaluation with a cost model and a simulation model.

Therefore, the shared data can be submitted to a low modification rate, while the rate of node arrival/departure is very important. We propose now solutions for that peer dynamicity. Peer Arrival. When a new peer p joins the system, it contacts some existing peers to determine the set of its neighbors. If one of those neighbors is a partner peer, p becomes a new partner: a new element is added to the cooperation list with a freshness value v equal to one. Recall that the value 1 indicates the need of pulling the peer to get new data descriptions.

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