Philosophiae Doctor - PhD / The high rate of grid computing adoption, both in academe and industry, has posed challenges regarding efficient access control, interoperability and scalability. Although several methods have been proposed to address these grid computing challenges, none has proven to be completely efficient and dependable. To tackle these challenges, a novel access control architecture framework, a triple-domain grid-based environment, modelled on role based access control, was developed. The architecture’s framework assumes three domains, each domain with an independent Local Security Monitoring Unit and a Central Security Monitoring Unit that monitors security for the entire grid.The architecture was evaluated and implemented using the G3S, grid security services simulator, meta-query language as “cross-domain” queries and Java Runtime Environment 1.7.0.5 for implementing the workflows that define the model’s task. The simulation results show that the developed architecture is reliable and efficient if measured against the observed parameters and entities. This proposed framework for access control also proved to be interoperable and scalable within the parameters tested.

Data 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.

Data 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.

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Data 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.

Data 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.

This thesis focuses on fluid simulation, particularly on coupling between particle based simulation and grid based simulation and thus modeling evaporation. Mentioned coupling is based on the article Evaporation and Condensation of SPH-based Fluids of authors Hendrik Hochstetter a Andreas Kolb. The goal of this thesis is not purely implementing ideas of the mentioned article, but also study of different methods used for fluid simulation.

Generating all hexahedral meshes on arbitrary geometries has been an area of important research in recent history. Hexahedral meshes have advantages over tetrahedral meshes in structural mechanics because they provide more accurate results with fewer degrees of freedom. Many different approaches have been used to create all-hexahedral meshes. Grid-based, inside-out, or superposition meshing all refer to a similar meshing approach that is a very common mesh generation technique. Grid-based algorithms provide the ability to generate all hexahedral meshes by introducing a structured mesh that bounds the complete body modeled, marking hexahedra to define an interior and exterior mesh, manipulating the boundary region between interior and exterior regions of the structured mesh to fit the specific boundary of the body, and finally, discarding the exterior hexahedra from the given body. Such algorithms generally provide high quality meshes on the interior of the body yet distort element at the boundary in order to fill voids and match surfaces along these regions. The sculpting algorithm as presented here, addresses the difficulty in forming quality elements near boundary regions in two ways. The algorithm first finds more intelligent methods to define a structured mesh that conforms to the body to lessen large distortions to the boundary elements. Second, the algorithm uses collapsing templates to adjust the position of boundary elements to mimic the topology of the body prior to capturing the geometric boundary.

Drones flying in squad formation while interconnected in an ad-hoc fashion are called Flying Ad hoc Networks (FANETs). These FANETs are gathering special interests in the networking community in their deployment for different vital missions. Such missions include rescue missions in case of disasters, monitoring and border control, animal monitoring, crowd monitoring and management, etc. The main problems researched with FANETs are typically inherited from what has been done for mobile ad-hoc Networks (MANETs) and Vehicular Ad-hoc Networks (VANETs) earlier. One of the major problems is routing and forwarding gathered data towards the member(i.e., the drone) closest to the sink or the member that gateways to the Internet to reach the sink. Clustering the FANET nodes (i.e., the drones) is found to be a good solution for this problem. The preeminent contributions of this thesis include a novel grinding technique of the geolocation where FANET is deployed to perform certain tasks, a grid-based mobility model for UAVs, and extending the EMASS algorithm so that it can adapt to our proposed grid-based system. The result proves our mobility model’s superiority over one of the most used mobility models, Random walk.

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