基于网格的分布式仿真系统关键技术研究
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摘要
随着科学技术的不断发展，人们对于建模及仿真技术的需求变得越来越迅猛。建模及仿真技术开始成为人们研究自然、探寻生物奥秘、开拓航空领域、翱翔宇宙等活动的重要工具，也成为人们进行信息资源共享、消除信息孤岛的重要手段。在不断增长的军事、商业、科研需求推动下，建模及仿真技术得到了迅猛发展。传统的分布式仿真技术缺乏动态负载平衡能力，不能共享各类仿真资源，计算资源利用率低下，难以扩展，不能适应大规模复杂的仿真需求。随着仿真规模的扩大和要求的不断提高，这些问题越来越成为分布式仿真技术发展的瓶颈。
针对传统分布式仿真技术存在的不足，这里提出了基于网格服务的高层体系结构HLA(High LevelArchitecture)。此体系结构分为两部分：一部分为基于网格的运行支撑环境RTI(Run Time Infrastructure)模块，另一部分是面向资源整合及其RTI调度的管理中心模块。在此基础上，提出了基于代理的请求回调机制、基于网格的仿真模型规范以及基于网格服务的仿真容错机制。这种将网格技术与分布式仿真技术相结合的策略为构建基于网格服务的高层体系结构提供了解决方法，具有很大的实用性。
针对现有仿真技术被动地选择任务和服务资源，缺少对服务资源的服务特性和服务能力的约束说明等问题，文章提出了一种基于网格的分布式仿真任务最优化调度机制。在此机制中，根据网格中服务的特性，建立了一种服务的最优化调度理论模型，将任务合理分配到仿真系统中的各节点机上，避免服务资源在给任务提供服务时出现性能瓶颈，有效解决了服务资源分配的不合理和系统扩展性不佳等问题。
针对系统服务质量越来越重要且资源越来越难以管理与控制等问题，文章提出了一种面向服务质量控制的资源管理机制。在此机制中，首先根据资源管理及其运行状态，设计了资源管理模型。在此基础上，提出了服务质量的预测方法和资源分配算法。这种机制较好地解决了系统资源服务质量不高、缺乏灵活性等问题。
针对常用的仿真数据收集方案在自适应性和管理监控方面的不足，根据网格环境下分布式仿真的特点，这里提出了一种新的数据收集机制。该机制包括：数据收集模型、数据收集的解析方法、数据收集的聚合方法、数据收集的存储方法以及数据收集模块、用户仿真应用模块、服务器端数据采集模块、用户数据采集模块等。在此机制中，采用了以服务为中心的构架，使基于网格的HLA分布式仿真具有良好的通用性、互操作性和重用性。
通过对上述问题的深入研究，以及所取得的相关研究成果，可以使基于网格的分布式仿真系统具有广域分布性、标准性、开放性和高可扩展性等特点。
With the rapid development of science and technology, it becomes more and morenecessary for researchers to use the techniques of modeling and simulation. The Modelingand simulation not only become an important tool in exploring the nature, discoverbiological mystery, opening aerospace field, and hovering the universe etc. but alsobecome an important method for people to share information resources and eliminate theisland of information. With the driving force from the sustained growth of the military, thebusiness and the requirement of scientific research, the modeling and simulationtechnology have experinced rapid development. Traditional distributed simulationtechnologies are not enough for people to share all kinds of simulation resources due tothe lack of load balance ability. Thus, the utilization rate of computational resource is lowand the system is hard to scale. Therefore, they can not adapt to the requirement of peoplefor large-scale complex simulations. With the expansion of the scale of simulation and theshart increase in requirements, these problems become the bottleneck in distributedsimulations.
In order to overcome the shortcoming of traditional distributed simulationtechnologies, we propose the architecture of the high-level support system for gridservices. We divide the architecture into two parts: the grid-based RTI module and theinformation integration and management center module of RTI scheduling. Based on thearchitecture, we further propose the agent-based request callback mechanism, thegrid-based simulation module standard and the fault mechanism of grid-based simulationservice. The combination of the grid technology and the distributed simulation technologyprovides a solution for building the high-level support system of grid service. Therefore,the strategy is very practical in real applications.
Existing simulation technologies usually passively choose tasks and service resourcesand lack the description of service characteristics of resources and the constraints ofservice ability. To cope with the problems, we propose an optimization mechanism forgrid-based distributed simulation tasks scheduling. In the mechanism, we first propose anoptimization scheduling theory model about service based on the characteristics of grid service. Then, we use the model to distribute tasks. Thus, it is impossible that the problemof performance bottleneck appears when the service resources are providing services fordifferent tasks. By using such mechanism, the problem about the unreasonable allocationof service resources and the poor scalability of system can be resolved.
To address the problem of how to control the quality of service of system and managethe system resources, we propose a resource management mechanism mainly consideringthe quality of service. In the mechanism, we first set up a resource management modelbased on the resource management and its operation state. Based on the model, wepropose the forecast method about the quality of service of the system and the resourceallocation algorithm. All these constitute the management mechanism of system resourcesoriented the quality of service of system. By using the mechanism, the problem includingthe low quality of service and the lack of flexibility are resolved.
In order to overcome the shortcoming of the current collection scheme of simulationdata, we propose a data resource mechanism according to the characteristics of grid-baseddistributed simulation. Specifically, in the mechanism, there is a data collection model, ananalysis method for data collection, a polymetization method for data collection, a storagemethod for data collection, a data collection module, a simulation application module ofclient, a data collection module of server, a data collection module of client, etc. In themechanism, the service is the main function. This makes the grid-based HLA distributionsimulation system have the good generality, interoperability and reusability.
All in all, the system will have the features of high-level distribution, standards, highscalability and so on based on the above research results in the grid-based distributedsimulation system.
针对传统分布式仿真技术存在的不足，这里提出了基于网格服务的高层体系结构HLA(High LevelArchitecture)。此体系结构分为两部分：一部分为基于网格的运行支撑环境RTI(Run Time Infrastructure)模块，另一部分是面向资源整合及其RTI调度的管理中心模块。在此基础上，提出了基于代理的请求回调机制、基于网格的仿真模型规范以及基于网格服务的仿真容错机制。这种将网格技术与分布式仿真技术相结合的策略为构建基于网格服务的高层体系结构提供了解决方法，具有很大的实用性。
针对现有仿真技术被动地选择任务和服务资源，缺少对服务资源的服务特性和服务能力的约束说明等问题，文章提出了一种基于网格的分布式仿真任务最优化调度机制。在此机制中，根据网格中服务的特性，建立了一种服务的最优化调度理论模型，将任务合理分配到仿真系统中的各节点机上，避免服务资源在给任务提供服务时出现性能瓶颈，有效解决了服务资源分配的不合理和系统扩展性不佳等问题。
针对系统服务质量越来越重要且资源越来越难以管理与控制等问题，文章提出了一种面向服务质量控制的资源管理机制。在此机制中，首先根据资源管理及其运行状态，设计了资源管理模型。在此基础上，提出了服务质量的预测方法和资源分配算法。这种机制较好地解决了系统资源服务质量不高、缺乏灵活性等问题。
针对常用的仿真数据收集方案在自适应性和管理监控方面的不足，根据网格环境下分布式仿真的特点，这里提出了一种新的数据收集机制。该机制包括：数据收集模型、数据收集的解析方法、数据收集的聚合方法、数据收集的存储方法以及数据收集模块、用户仿真应用模块、服务器端数据采集模块、用户数据采集模块等。在此机制中，采用了以服务为中心的构架，使基于网格的HLA分布式仿真具有良好的通用性、互操作性和重用性。
通过对上述问题的深入研究，以及所取得的相关研究成果，可以使基于网格的分布式仿真系统具有广域分布性、标准性、开放性和高可扩展性等特点。
With the rapid development of science and technology, it becomes more and morenecessary for researchers to use the techniques of modeling and simulation. The Modelingand simulation not only become an important tool in exploring the nature, discoverbiological mystery, opening aerospace field, and hovering the universe etc. but alsobecome an important method for people to share information resources and eliminate theisland of information. With the driving force from the sustained growth of the military, thebusiness and the requirement of scientific research, the modeling and simulationtechnology have experinced rapid development. Traditional distributed simulationtechnologies are not enough for people to share all kinds of simulation resources due tothe lack of load balance ability. Thus, the utilization rate of computational resource is lowand the system is hard to scale. Therefore, they can not adapt to the requirement of peoplefor large-scale complex simulations. With the expansion of the scale of simulation and theshart increase in requirements, these problems become the bottleneck in distributedsimulations.
In order to overcome the shortcoming of traditional distributed simulationtechnologies, we propose the architecture of the high-level support system for gridservices. We divide the architecture into two parts: the grid-based RTI module and theinformation integration and management center module of RTI scheduling. Based on thearchitecture, we further propose the agent-based request callback mechanism, thegrid-based simulation module standard and the fault mechanism of grid-based simulationservice. The combination of the grid technology and the distributed simulation technologyprovides a solution for building the high-level support system of grid service. Therefore,the strategy is very practical in real applications.
Existing simulation technologies usually passively choose tasks and service resourcesand lack the description of service characteristics of resources and the constraints ofservice ability. To cope with the problems, we propose an optimization mechanism forgrid-based distributed simulation tasks scheduling. In the mechanism, we first propose anoptimization scheduling theory model about service based on the characteristics of grid service. Then, we use the model to distribute tasks. Thus, it is impossible that the problemof performance bottleneck appears when the service resources are providing services fordifferent tasks. By using such mechanism, the problem about the unreasonable allocationof service resources and the poor scalability of system can be resolved.
To address the problem of how to control the quality of service of system and managethe system resources, we propose a resource management mechanism mainly consideringthe quality of service. In the mechanism, we first set up a resource management modelbased on the resource management and its operation state. Based on the model, wepropose the forecast method about the quality of service of the system and the resourceallocation algorithm. All these constitute the management mechanism of system resourcesoriented the quality of service of system. By using the mechanism, the problem includingthe low quality of service and the lack of flexibility are resolved.
In order to overcome the shortcoming of the current collection scheme of simulationdata, we propose a data resource mechanism according to the characteristics of grid-baseddistributed simulation. Specifically, in the mechanism, there is a data collection model, ananalysis method for data collection, a polymetization method for data collection, a storagemethod for data collection, a data collection module, a simulation application module ofclient, a data collection module of server, a data collection module of client, etc. In themechanism, the service is the main function. This makes the grid-based HLA distributionsimulation system have the good generality, interoperability and reusability.
All in all, the system will have the features of high-level distribution, standards, highscalability and so on based on the above research results in the grid-based distributedsimulation system.
引文
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