Introduction to algorithms
Optimizing cell-size in grid-based DDM
PADS '00 Proceedings of the fourteenth workshop on Parallel and distributed simulation
Dynamic Grid-Based Multicast Group Assignment in Data Distribution Management
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
A sort-based DDM matching algorithm for HLA
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Performance evaluation of Data Distribution Management strategies: Research Articles
Concurrency and Computation: Practice & Experience - Distributed Simulation and Real-Time Applications
An Efficient Sort-Based DDM Matching Algorithm for HLA Applications with a Large Spatial Environment
Proceedings of the 21st International Workshop on Principles of Advanced and Distributed Simulation
Interest management for distributed virtual environments: A survey
ACM Computing Surveys (CSUR)
A Parallel Data Distribution Management Algorithm
DS-RT '13 Proceedings of the 2013 IEEE/ACM 17th International Symposium on Distributed Simulation and Real Time Applications
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Data Distribution Management (DDM) is one of the High Level Architecture (HLA) services that reduce message traffic over the network. The major purpose of the DDM is to filter the exchange of data between federates during a federation. However, this traffic reduction usually suffers from higher computational overhead when calculating the intersection between update regions and subscription regions in a matching process. In order to reduce the computational overhead for the matching process, this paper proposes a binary partition-based matching algorithm for DDM in the HLA-based distributed simulation. The new matching algorithm is fundamentally based on a divide-and-conquer approach. The proposed algorithm recursively performs binary partitioning which divides the regions into two partitions that entirely cover those regions. This approach promises low computational overhead, since it does not require unnecessary comparisons within regions in different partitions. The experimental results show that the proposed algorithm performs better than the existing DDM matching algorithms and improves the scalability of the DDM.