Distributed simulation is an enabling concept to support the networked interaction of models and real world elements that are geographically distributed. This technology has brought a new set of challenging problems to solve, such as Data Distribution Management (DDM). The aim of DDM is to limit and control the volume of the data exchanged during a distributed simulation, and reduce the processing requirements of the simulation hosts by relaying events and state information only to those applications that require them. In this thesis, we propose a new DDM scheme, which we refer to as dynamic grid-based DDM. A lightweight UNT-RTI has been developed and implemented to investigate the performance of our DDM scheme. Our results clearly indicate that our scheme is scalable and it significantly reduces both the number of multicast groups used, and the message overhead, when compared to previous grid-based allocation schemes using large-scale and real-world scenarios.

The High Level Architecture (HLA) is a prototype architecture for constructing distributed simulations. HLA is a standard adopted by the Department of Defense (DOD) for development of simulation environments. An important goal of the HLA is to reduce the amount of data routing between simulations during run-time. The Runtime Infrastructure (RTI) is an operating system that is responsible for data routing between the simulations in HLA. The data routing service is provided by the Data Distribution Manager of the RTI. Several methods have been proposed and used for the implementation of data distribution services. The grid-based filtering method, the interval tree method, and the quad-tree method are examples. This thesis analyzes and compares two such methods: the grid and the quad-tree, in regards to their use in the discovery of intersections of publications and subscriptions. The number of false positives and the CPU time of each method are determined for typical cases. For most cases, the quad-tree methos produces less false positives. This method is best suited for large simulations where the cost of maintaining false positives, or non-relevant entities, may be prohibitive. For most cases, the grid method is faster than the quad-tree method. This method may be better suited for small simulations where the host has the capacity to accommodate false positives. The results of this thesis can be used to decide which of the two methods is better suited to a particular type of simulation exercise.

In this dissertation, we present an efficient algorithm, called P-Pruning algorithm, for data distribution management problem in High Level Architecture. High Level Architecture (HLA) presents a framework for modeling and simulation within the Department of Defense (DoD) and forms the basis of IEEE 1516 standard. The goal of this architecture is to interoperate multiple simulations and facilitate the reuse of simulation components. Data Distribution Management (DDM) is one of the six components in HLA that is responsible for limiting and controlling the data exchanged in a simulation and reducing the processing requirements of federates. DDM is also an important problem in the parallel and distributed computing domain, especially in large-scale distributed modeling and simulation applications, where control on data exchange among the simulated entities is required. We present a performance-evaluation simulation study of the P-Pruning algorithm against three techniques: region-matching, fixed-grid, and dynamic-grid DDM algorithms. The P-Pruning algorithm is faster than region-matching, fixed-grid, and dynamic-grid DDM algorithms as it avoid the quadratic computation step involved in other algorithms. The simulation results show that the P-Pruning DDM algorithm uses memory at run-time more efficiently and requires less number of multicast groups as compared to the three algorithms. To increase the scalability of P-Pruning algorithm, we develop a resource-efficient enhancement for the P-Pruning algorithm. We also present a performance evaluation study of this resource-efficient algorithm in a memory-constraint environment. The Memory-Constraint P-Pruning algorithm deploys I/O efficient data-structures for optimized memory access at run-time. The simulation results show that the Memory-Constraint P-Pruning DDM algorithm is faster than the P-Pruning algorithm and utilizes memory at run-time more efficiently. It is suitable for high performance distributed simulation applications as it improves the scalability of the P-Pruning algorithm by several order in terms of number of federates. We analyze the computation complexity of the P-Pruning algorithm using average-case analysis. We have also extended the P-Pruning algorithm to three-dimensional routing space. In addition, we present the P-Pruning algorithm for dynamic conditions where the distribution of federated is changing at run-time. The dynamic P-Pruning algorithm investigates the changes among federates regions and rebuilds all the affected multicast groups. We have also integrated the P-Pruning algorithm with FDK, an implementation of the HLA architecture. The integration involves the design and implementation of the communicator module for mapping federate interest regions. We provide a modular overview of P-Pruning algorithm components and describe the functional flow for creating multicast groups during simulation. We investigate the deficiencies in DDM implementation under FDK and suggest an approach to overcome them using P-Pruning algorithm. We have enhanced FDK from its existing HLA 1.3 specification by using IEEE 1516 standard for DDM implementation. We provide the system setup instructions and communication routines for running the integrated on a network of machines. We also describe implementation details involved in integration of P-Pruning algorithm with FDK and provide results of our experiences.

Logical time management is used to synchronize the executions of distributed simulation elements. In existing time management systems, such as High Level Architecture (HLA), logical times of the simulation elements are synchronized. However, in some cases synchronization can unnecessarily decrease the performance of the system. In the proposed HLA based time management mechanism, federates are clustered into logically related groups. The relevance of federates is taken to be a function of proximity which is defined as the distance between them in the virtual space. Thus, each federate cluster is composed of relatively close federates according to calculated distances. When federate clusters are sufficiently far from each other, there is no need to synchronize them, as they do not relate each other. So in PATiM mechanism, inter-cluster logical times are not synchronized when clusters are sufficiently distant. However, if the distant federate clusters get close to each other, they will need to resynchronize their logical times. This temporal partitioning is aimed at reducing network traffic and time management calculations and also increasing the concurrency between federates. The results obtained based on case applications have verified that clustering improves local performance as soon as federates become unrelated.

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.

Data distribution management (DDM) is a mechanism to interconnect data producers and data consumers in a distributed application. Data producers provide useful data to consumers in the form of messages. For each message produced, DDM determines the set of data consumers interested in receiving the message and delivers it to those consumers. We are particularly interested in DDM techniques for parallel and distributed discrete event simulations. Thus far, researchers have treated synchronization of events (i.e. time management) and DDM independent of each other. This research focuses on how to realize time managed DDM mechanisms. The main reason for time-managed DDM is to ensure that changes in the routing of messages from producers to consumers occur in a correct sequence. Also time managed DDM avoids non-determinism in the federation execution, which may result in non-repeatable executions. An optimistic approach to time managed DDM is proposed where one allows DDM events to be processed out of time stamp order, but a detection and recovery procedure is used to recover from such errors. These mechanisms are tailored to the semantics of the DDM operations to ensure an efficient realization. A correctness proof is presented to verify the algorithm correctly synchronizes DDM events. We have developed a fully distributed implementation of the algorithm within the framework of the Georgia Tech Federated Simulation Development Kit (FDK) software. A performance evaluation of the synchronized DDM mechanism has been completed in a loosely coupled distributed system consisting of a network of workstations connected over a local area network (LAN). We compare time-managed versus unsynchronized DDM for two applications that exercise different mobility patterns: one based on a military simulation and a second utilizing a synthetic workload. The experiments and analysis illustrate that synchronized DDM performance depends on several factors: the simulations model (e.g. lookahead), applications mobility patterns and the network hardware (e.g. size of network buffers). Under certain mobility patterns, time-managed DDM is as efficient as unsynchronized DDM. There are also mobility patterns where time-managed DDM overheads become significant, and we show how they can be reduced.

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.