HLA/RTI仿真平台的数据分发管理

在基于 HLA/RTI 的大规模交互仿真中,如何高效地实现数据分发管理的信息交互和传递机制是分布式交互仿真的重要内容。介绍了 HLA 仿真中的联邦开发和执行过程模型和数据分发管理,提出了一种新的数据分发管理算法,并阐述了该算法的理论和具体实现方法。通过分析采用该算法的数据分发管理仿真系统,证明该算法有效的提高了数据过滤效率,从而缩短系统仿真时间。     

HLA/RTI的数据管理方法分析

分布交互式仿真代表了仿真技术的发展方向。未来的分布式仿真是建立在因特网环境下的,减少网络负荷成为分布式仿真的一个重要课题。高级体系结构HLA是美国国防部提出的,在军事仿真方面有着广阔的应用前景。其目的是解决各仿真系统之间的互操作和可重用,HLA提供了声明管理和数据分发管理服务来进行数据的分发,使联邦成员只接收它感兴起的信息,从而减少上网的数据量。该文在对比HIA与DIS的数据管理方法的基础上,简述了DIS数据管理方法的特点及其局限,着重分析了HLA中的声明管理与数据分发管理的基本原理。     

基于Web的HLA仿真系统研究

该文介绍了基于Web的仿真的优点，比较了本地执行、远地执行、本地和远地相结合三种基于Web的仿真实现方案和存在的问题。提出了基于Web的HLA(High Level Architecture)的仿真系统(WBHLA)，实现了浏览器层、Web服务器层、应用服务器层、HLA仿真层的四层体系结构。利用XML技术实现Web系统与HLA系统之间的数据接口，解决浏览器／服务器之间的大数据量交互和仿真想定数据在仿真盟员之间的分发两大问题。通过将与用户交互相关的运行管理盟员、想定管理盟员、数据采集盟员封装成COM组件，实现Web出系统与HLA系统之间的桥接。最后提出Web服务技术实现基于Web的仿真系统的研究方向。     

数据分发管理机制中层次化组播地址分配策略

高层体系结构HLA(high level architecture)支持仿真应用间的互操作和可重用,受资源的限制,基于因特网的分布仿真面临着系统可扩缩性的挑战.HLA提供数据分发管理机制,为提高系统可扩缩性提供了可能.分析了HLA中数据分发管理机制的实现途径,针对存在的问题提出了层次化的组播地址分配策略,解决了组播地址数量有限与仿真结点机冗余数据接收之间的矛盾,同时也为仿真数据的可靠发送及数据打包提供了有力的支持.     

HLA/RTI中记录和重演问题研究

记录和重演是HLA／RTI仿真框架中应用的重要方面之一，该文在HLA／RTI体系结构入手，讨论了记录和重演的两种可行结构集中式和分布式的各自优缺点，提出了一种新的对象ID管理概念，以及如何利用声明管理和数据分发管理服务来解决从DIS系统过渡到RTI时记录重演遇到的问题。     

关于HLA中声明管理和数据分发管理的研究

HLA是一种构建分布式仿真应用的体系结构。其中的声明管理服务，建立了有效的通信连接．减少了发送给联邦成员的数据量。本文指出这种分发是面向全部联邦成员的．且贯穿仿真的全过程；当参与仿真的联邦成员增多时，还会发生数据堵塞。HLA中的数据分发管理可以更加具体地、定时地、针对特定的联邦成员发布／订阅数据，从而进一步减少了数据的传输。本文论述了这两者区别与联系，保证了在工程应用中正确地、合理地使用它们。     

DIS与HLA的数据分发机制研讨

高级体系结构HLA是美国国防部提出的，在军事仿真方面有着广阔的应用前景，其目的是解决仿真系统之间的互操作和可重用，代表着仿真技术发展的趋势，该文简述了DIS的特点及其局限，对HLA的发展背景，优势进行了论述，着重讨论了HLA中于组播通讯的数据分发机制。     

基于HLA和Agent的电子对抗仿真系统构建

综合运用基于HLA和Agent的建模仿真技术,设计开发分布式的仿真联邦,研究基于HLA的Ageng通信控制、本体模型在Agent知识水平通信中的应用以及提高HLA仿真效率途径等问题。研究结果表明,集成的HLA和Agent仿真环境能够支持复杂系统对抗的分析应用。     

HLA中数据分发管理的探究

HLA已成为了现代分布式仿真应用的通用技术框架的核心,而作为其六大服务之一的数据分发管理则提供了有效的信息交互和传送机制以满足系统可扩缩性的要求。文章介绍了HLA中数据分发管理DDM的过滤原理;研究并分析了目前几种实现DDM过滤机制的方法。     

基于颜色Petri网和Lookahead的数据分发管理研究

对高层体系结构（HLA）中数据分发管理（DDM）的研究,主要目标是在符合HLA标准的前提下,提高数据的过滤效率,同时减少计算量,并提供较好的可扩展性适应于各种规模的分布式仿真应用。Lookahead是分布式模拟时间管理协议中的一个重要概念,各模拟实体使用Lookahead把自己产生事件的时间标记情况更早地通知给其它实体,以加快程序的运行。采用颜色Petri网和Lookahead的数据分发机制,能够很好地对数据进行过滤,提高数据的过滤效率,并能保证数据收发的成功率。仿真实验结果表明：基于颜色Petri网和Lookahead的数据分发机制是优于区域匹配方法的。     

大型分布交互式仿真系统的DDM策略研究

分布交互式仿真是未来仿真技术发展的方向.大型分布交互式仿真应用具有仿真实体数目较大、实体间信息交互频繁等特点,而分布仿真应用基于的网络带宽资源又十分有限,因此如何降低网络冗余数据,充分利用网络带宽资源以提高网络上的有效数据的传输效率成为一个重要的研究方向.数据分发管理DDM的目标是滤除仿真运行中网络上的无用数据,减少网络上的数据.文中简单地介绍了HLA的基本思想,阐述了路径空间的概念,并结合多目组通讯技术,描述了几种实用的DDM策略,比较了它们的异同点,为选用合适的数据滤除方法管理大型分布仿真的大规模数据提供了依据.     

基于网格的最优网格数据过滤机制研究及实现

该文总结了高层体系结构（HLA／RTI）中数据分发管理（DDM）目前常用的几种机制,并分析对比了它们各自的优缺点。在此基础上给出了基于网格技术的最优网格的数据过滤机制（Best—Grid—Based Filtering）的实现方法,给出了它的理论模型及其计算公式,并将这种数据过滤机制运用于森林协同灭火子系统中。通过试验验证,最优网格数据过滤机制实现了高层体系结构中数据分发管理在局域网上数据过滤的高效性,有效地减少了网络的冗余负载量提高了带宽的有效利用,进而满足了交互仿真的实时性与逻辑正确性。     

Mobile agents for information retrieval in hybrid simulation environment

In this paper, we propose a hybrid simulation environment that incorporates with wired/wireless networks, IEEE standard 1516 high-level architecture (HLA), and IBM Aglets mobile agent system. Therefore, HLA simulations are not restricted to be participated solely by using desktop computers with cable connections. Users can use a wide variety of devices to join in HLA simulations and explicitly exclude from junk data in terms of a personalized data filtering policy. Based on data correlation between HLA objects and a client's data filtering policy, we employ the simulation environment manager in distributing a client to an appropriate federate server (FS). In particular, a mobile agent, namely data filtering agent, is devised to temporarily reside at the FS to perform mobile agent-based data distribution management for clients. As a result, the clients can receive the most interested information corresponding to their pre-defined data filtering policies. Once either the data transmission quality within the wireless network is degraded below a threshold or the clients abnormally modify the data filtering policies, their own mobile agents carry out migrations to provide the users with the ubiquitous and seamless services. Consequently, the users can use any mobile device as well as using a desktop computer in a stationary point to participate in the HLA simulations. The experimental results also show that the proposed mobile agent-based data distribution can raise adaptability and applicability to large-scale HLA simulations.     

Offloading Data Distribution Management to Network Processors in HLA-Based Distributed Simulations

The high-level architecture (HLA) standard developed by the Department of Defense in the United States is a key technology to perform distributed simulation. Inside the HLA framework, many different simulators (termed federates) may be interconnected to create a single more complex simulator (federation). Data distribution management (DDM) is an optional subset of services that controls which federates should receive notification of state modifications made by other federates. A simple DDM implementation will usually generate much more traffic than needed, whereas a complex one might introduce too much overhead. In this work, we describe an approach to DDM that delegates a portion of the DDM computation to a processor on the network card in order to provide more CPU time for other federate and Runtime Infrastructure (RTI) computations while still being able to exploit the benefits of a complex DDM implementation to reduce the amount of information exchange.     

Scalability and Performance Evaluation of DDM-Based Aggregation/Dissaggregation Protocols for Large-Scale Distributed Interactive Simulations Systems

Aggregation/disaggregation is a method for implementing multi-resolution simulations within a High Level Architecture (HLA) federation. HLA is a U.S. Department of Defense (DoD) developed standard to facilitate linking different types of simulations, in various locations, to form an or interactive, full-scale simulation, called a federation. Data Distribution Management (DDM) is a High Level Architecture/Run-time Infrastructure (HLA/RTI) service that manages the distribution of state updates and interaction information and controls the volume of data exchanged, in large-scale distributed simulations. The purpose of HLA is to promote interoperability and reuse among heterogenous simulations, including those simulations that offer varied levels of resolution, to provide practical training to military personnel of different ranks. The purpose of Aggregation/disaggregation is to ensure consistency in state updates between federates simulating objects at various levels of resolution. This paper focuses on the scalability of aggregation/disaggregation with different DDM implementations and examines the effects, on performance of large-scale simulations. We implement a federate-based aggregation/disaggregation scheme, originally introduced in [TAN01], with a tank dogfight scenario, aggregating five tanks into one tank battalion and disaggregating the battalion back into five individual entities (tanks). The DDM methods we analyze consist of the Fixed Grid-Based method, the Dynamic Grid-Based method and the Region-Based method. In [TAN01], testing of this federate-based aggregation/disaggregation was limited to a dual federation and a single DDM scheme. In an effort to determine the scalability of aggregation/disaggregation, with three methods of DDM, we measure the communication overhead and analyze performance during a federation execution. We present the results of extensive testing, varying the number of aggregation/disaggregation requests, the number of multi-resolution federates participating in the federation, the number of objects, the number/size of the grids and report on the performance evlauation of our protocols using an extensive set of simulation experiments. This work was partially supported by Grants from NSERC, Canada Research Chairs Program, Canada Foundation for Innovation, OIT/Distinguished Researcher Award.     

HLA/RTI中DDM小模型的实现

该文简要介绍了HLA中DDM存在的必要性 ,之后从一般分布式的观点分析了HLA及DDM实现的原理 ,同时给出了存在DDM的情况下 ,HLA中数据公布和订购的基本流程。最后介绍了DDM已知的实现方法和我们的实现方法 ,同时详细地给出了对象属性和交互的请求应答机制。     

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

Data distribution management (DDM) plays a key role in traffic control for 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, and dynamic grid-based (DGB) schemes, as well as grid-filtered region-based and agent-based DDM 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 the 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 DR represents the relative processing load and communication load generated at each federate. The DR and the matching performance 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 DGB scheme. The novelty of the ADGB scheme is its focus on improving performance, an important (and often forgotten) goal of DDM strategies.