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Service-Oriented Model Encapsulation and Selection Method for Complex System Simulation Based on Cloud Architecture

机译:基于云架构的面向服务的复杂系统仿真模型封装与选择方法

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With the rise in cloud computing architecture, the development of service-oriented simulation models has gradually become a prominent topic in the field of complex system simulation. In order to support the distributed sharing of the simulation models with large computational requirements and to select the optimal service model to construct complex system simulation applications, this paper proposes a service-oriented model encapsulation and selection method. This method encapsulates models into shared simulation services, supports the distributed scheduling of model services in the network, and designs a semantic search framework which can support users in searching models according to model correlation. An optimization selection algorithm based on quality of service (QoS) is proposed to support users in customizing the weights of QoS indices and obtaining the ordered candidate model set by weighted comparison. The experimental results showed that the parallel operation of service models can effectively improve the execution efficiency of complex system simulation applications, and the performance was increased by 19.76% compared with that of scatter distribution strategy. The QoS weighted model selection method based on semantic search can support the effective search and selection of simulation models in the cloud environment according to the user’s preferences.
机译:随着云计算架构的兴起,面向服务的仿真模型的开发已逐渐成为复杂系统仿真领域中的一个突出主题。为了支持具有较大计算需求的仿真模型的分布式共享,并为构建复杂的系统仿真应用选择最佳的服务模型,提出了一种面向服务的模型封装与选择方法。该方法将模型封装到共享的仿真服务中,支持网络中模型服务的分布式调度,并设计了一种语义搜索框架,可以支持用户根据模型相关性搜索模型。提出了一种基于服务质量(QoS)的优化选择算法,以支持用户定制QoS指标的权重,并通过加权比较获得有序候选模型集。实验结果表明,服务模型的并行操作可以有效地提高复杂系统仿真应用程序的执行效率,与分散分布策略相比,性能提高了19.76%。基于语义搜索的QoS加权模型选择方法可以支持根据用户的偏好在云环境中有效地搜索和选择仿真模型。

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