Data Distribution Management In Large-scale Distributed Environments

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dc.contributor.authorGu, Yunfeng
dc.date.accessioned2012-02-15T15:54:27Z
dc.date.available2012-02-15T15:54:27Z
dc.date.created2012
dc.date.issued2012
dc.identifier.urihttp://hdl.handle.net/10393/20691
dc.identifier.urihttp://dx.doi.org/10.20381/ruor-5483
dc.description.abstractData Distribution Management (DDM) deals with two basic problems: how to distribute data generated at the application layer among underlying nodes in a distributed system and how to retrieve data back whenever it is necessary. This thesis explores DDM in two different network environments: peer-to-peer (P2P) overlay networks and cluster-based network environments. DDM in P2P overlay networks is considered a more complete concept of building and maintaining a P2P overlay architecture than a simple data fetching scheme, and is closely related to the more commonly known associative searching or queries. DDM in the cluster-based network environment is one of the important services provided by the simulation middle-ware to support real-time distributed interactive simulations. The only common feature shared by DDM in both environments is that they are all built to provide data indexing service. Because of these fundamental differences, we have designed and developed a novel distributed data structure, Hierarchically Distributed Tree (HD Tree), to support range queries in P2P overlay networks. All the relevant problems of a distributed data structure, including the scalability, self-organizing, fault-tolerance, and load balancing have been studied. Both theoretical analysis and experimental results show that the HD Tree is able to give a complete view of system states when processing multi-dimensional range queries at different levels of selectivity and in various error-prone routing environments. On the other hand, a novel DDM scheme, Adaptive Grid-based DDM scheme, is proposed to improve the DDM performance in the cluster-based network environment. This new DDM scheme evaluates the input size of a simulation based on probability models. The optimum DDM performance is best approached by adapting the simulation running in a mode that is most appropriate to the size of the simulation.
dc.language.isoen
dc.publisherUniversité d'Ottawa / University of Ottawa
dc.subjectData Distribution Management
dc.subjectDDM
dc.subjectRange Query
dc.subjectAssociative searching
dc.subjectMulti-dimensional
dc.subjectSimulation
dc.subjectDistributed
dc.subjectP2P
dc.subjectHLA/RTI
dc.subjectCluster
dc.subjectData structure
dc.subjectOverlay
dc.subjectRegion-based
dc.subjectGrid-based
dc.subjectHD Tree
dc.subjectAGB DDM
dc.titleData Distribution Management In Large-scale Distributed Environments
dc.typeThesis
dc.faculty.departmentÉcole de science informatique et de génie électrique / School of Electrical Engineering and Computer Science
dc.contributor.supervisorBoukerche, Azzedine
dc.embargo.termsimmediate
dc.degree.namePhD
dc.degree.leveldoctorate
dc.degree.disciplineGénie / Engineering
thesis.degree.namePhD
thesis.degree.levelDoctoral
thesis.degree.disciplineGénie / Engineering
uottawa.departmentÉcole de science informatique et de génie électrique / School of Electrical Engineering and Computer Science
CollectionThèses, 2011 - // Theses, 2011 -

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