Three-tiered interest management for large-scale virtual environments
VRST '98 Proceedings of the ACM symposium on Virtual reality software and technology
Dynamic grid-based approach to data distribution management
Journal of Parallel and Distributed Computing - Parallel and Distributed Discrete Event Simulation--An Emerging Technology
High Level Architecture for Simulation: An Update
DIS-RT '98 Proceedings of the Second International Workshop on Distributed Interactive Simulation and Real-Time Applications
An Agent-Based DDM Filtering Mechanism
MASCOTS '00 Proceedings of the 8th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems
VRAIS '95 Proceedings of the Virtual Reality Annual International Symposium (VRAIS'95)
Design of High Performance RTI Software
DS-RT '00 Proceedings of the Fourth IEEE International Workshop on Distributed Simulation and Real-Time Applications
A Hybrid Approach to Data Distribution Management
DS-RT '00 Proceedings of the Fourth IEEE International Workshop on Distributed Simulation and Real-Time Applications
ANSS '05 Proceedings of the 38th annual Symposium on Simulation
Performance Analysis of an Adaptive Dynamic Grid-Based Approach to Data Distribution Management
DS-RT '06 Proceedings of the 10th IEEE international symposium on Distributed Simulation and Real-Time Applications
| Hi-index | 0.00 |
The goal of this paper is to provide an optimal solution for Data Distribution Management (DDM) in large-scale distributed simulations. Until now, all existing DDM approaches have tried to make DDM more efficient in different ways; however, none has been able to optimize performance. The main reason for this inability is that these approaches manipulate the data generated in a simulation without evaluating the size of it. We propose a novel resource allocation scheme, the Adaptive Resource Allocation Control scheme (ARAC). The ARAC scheme is designed to optimize resource allocations for local and distributed processing work at each federate according to the size of the simulation. Efficiency is achieved by applying the analysis results of a static probability model, which we call the Matching Model. Performance comparisons between the existing grid-based approaches and the new adaptive approach show that the new scheme is much more flexible in adapting to various simulation sizes and comes much closer to an optimal solution. The novelty of the ARAC scheme is that it is able to scale the size of a simulation and control the simulation itself by running it in the most appropriate mode to achieve the desired efficiency. As a final result, the optimum performance is best approached.