Data distribution management migration from DoD 1.3 to IEEE 1516

Morse, Katherine L.; Petty, Mikel D.

doi:10.1109/distra.2001.946444

Cited by 25 publications

(18 citation statements)

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“…Another difference in the IEEE 1516 version of the interface specification is the elimination of extents to improve the accuracy of interaction notifications. We explain extents in further detail in Section 3.2 [8].…”

Section: Routing Spacementioning

confidence: 99%

“…In the versions of the interface specification, prior to IEEE 1516, an extent was given an ordered pair indicating the minimum and maximum range of a region across a routing space dimension. As previously mentioned, the IEEE 1516 version eliminates extents from the HLA specifications and replaces them with region sets [8]. An example of a subscription region is the radar range of a ground-based radar (GBR) simulator.…”

Section: Publication and Subscription Regionsmentioning

confidence: 99%

“…In addition to a team of DMSO employees, contracted companies and individuals directly involved in the HLA project, various companies and universities are independently researching methods to implement HLA efficiently and cost effectively. In the Fall of 2000, IEEE adopted the most recent versions 1516, 1516.1, 1516.2 of the HLA documents as an industry standard, broadening the HLA community even further [8].…”

Section: Hla and Rtimentioning

confidence: 99%

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Performance evaluation of Data Distribution Management strategies

Boukerche

Dzermajko

2004

Concurrency and Computation

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SUMMARYData Distribution Management (DDM) is a High Level Architecture/Run-Time Infrastructure (HLA/RTI) service that manages the distribution of state updates and interaction information in large-scale distributed simulations and limits and controls the volume of data exchanged during the simulation.In this paper, we describe a mini-RTI framework that we have developed in an effort to determine the most efficient model for applying the DDM service and the limitations of the scalability of various DDM methods. We study and compare the performance of the following five DDM strategies: two variations of the fixed-based method, two variations of the dynamics grid-based scheme and the region-based method. Due to a lack of accepted benchmarks, we also propose a variety of workloads and scenarios, which we hope will become a standards benchmark within the distributed simulation communities.

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Section: Routing Spacementioning

confidence: 99%

Section: Publication and Subscription Regionsmentioning

confidence: 99%

Section: Hla and Rtimentioning

confidence: 99%

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Performance evaluation of Data Distribution Management strategies

Boukerche

Dzermajko

2004

Concurrency and Computation

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“…In order to understand these concepts more thoroughly, we incorporate their different representations as used by two major DDM systems, known as the region-based system and the grid-based system. These two systems will be described in Section 3 [6,9,14,16].…”

Section: Basic Concepts Of Ddmmentioning

confidence: 99%

Performance analysis of an adaptive dynamic grid-based approach to data distribution management

Boukerche

Araújo

2008

Journal of Parallel and Distributed Computing

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“…Effectively controlling and managing this data without jeopardizing performance is a critical issue. Several DDM [11,14,16,17] schemes that run transparently in the simulation and filter the data exchanged between participants have been devised [12,19]. Examples of these include the Region-Based (RB) [8,10], Fixed Grid-Based (FGB) [1,2], Hybrid [21], Dynamic Grid-Based (DGB) [2,7,9] and Grid-Filtered Region-Based (GFRB) [3][4][5] approaches.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Dynamic Grid-Based approach to DDM for large-scale distributed simulation systems

Boukerche¹,

Gu²,

Araújo³

2008

Journal of Computer and System Sciences

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Data Distribution Management (DDM) plays a key role in traffic volume control of large-scale distributed simulations. In recent years, several solutions have been devised to make DDM more efficient and adaptive to different traffic conditions. Examples of such systems include the Region-Based, Fixed Grid-Based, Hybrid, and Dynamic Grid-Based (DGB) schemes. However, less effort has been directed toward improving the processing performance of DDM techniques. This paper presents a novel DDM scheme called the Adaptive Dynamic Grid-Based (ADGB) scheme that optimizes DDM time through analysis of matching performance. ADGB uses an advertising scheme in which information about the target cell involved in the process of matching subscribers to publishers is known in advance. An important concept known as the Distribution Rate (DR) is devised. The distribution rate represents the relative processing load and communication load generated at each federate. The matching performance and the distribution rate are used as part of the ADGB method to select, throughout the simulation, the devised advertisement scheme that achieves the maximum gain with acceptable network traffic overhead. If we assume the same worst case propagation delays, when the matching probability is high, the performance estimation of ADGB has shown that a maximum efficiency gain of 66% can be achieved over the Dynamic Grid-Based scheme. The novelty of the ADGB scheme is its focus on improving performance, an important (and often forgotten) goal of DDM strategies.

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Data distribution management migration from DoD 1.3 to IEEE 1516

Cited by 25 publications

References 0 publications

Performance evaluation of Data Distribution Management strategies

Performance evaluation of Data Distribution Management strategies

Performance analysis of an adaptive dynamic grid-based approach to data distribution management

An Adaptive Dynamic Grid-Based approach to DDM for large-scale distributed simulation systems

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