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dc.contributor.authorWang, Yuyingen
dc.contributor.authorLi, Hongmeien
dc.contributor.authorJia, Jinyuanen
dc.date.accessioned2015-08-17T09:40:02Z
dc.date.available2015-08-17T09:40:02Z
dc.date.issued2007
dc.identifier.citationWang, Y., Li, H. & Jia, J. (2007) An adaptive multi-mode downloading partitioning algorithm of distributed virtual environment based on grid computing. In: Image and Graphics, 2007. ICIG 2007. Fourth International Conference on, Sichuan, China, 22-24 Aug 2007, pp. 1026 - 1032.en
dc.identifier.isbn0-7695-2929-1
dc.identifier.urihttp://hdl.handle.net/2086/11137
dc.description.abstractWhen users are connected to a Large Scale distributed virtual environment system on internet, downloading speed is a key point to support a life-like world and real time interactions for a large number of avatars in a consistent fashion. We proposed an adaptive multi-node downloading partitioning algorithm to balance the bottleneck problem on multi- node downloading architecture we designed. It facilitates a flexible and scalable downloading large scale DVE. Our method has five major steps: (1) to build up a database by partitioning the ground of a DIE into two-dimensional square regions covered with three-dimensional objects uniformly; (2) to determine statically a region where an avatar's area of interest located according to its spatial coherence; (3) to group the grid nodes located on same region in DVE; (4) to build a downloading servers group within each region and adapt it dynamically; (5) to balance the downloading workload dynamically. A specific multi-node downloading component is devised for supporting remote multi-thread downloading DVE based Globus platform. The downloading speed is proportional to the number of grid nodes participated in the downloading task in each region. Initial experimental results show the feasibility and effectiveness of our approach.en
dc.language.isoenen
dc.publisherIEEEen
dc.titleAn adaptive multi-mode downloading partitioning algorithm of distributed virtual environment based on grid computing.en
dc.typeBook chapteren
dc.identifier.doihttps://doi.org/10.1109/ICIG.2007.182
dc.funderN/Aen
dc.projectidN/Aen


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