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首页> 外文期刊>ACM Transactions on Modeling and Computer Simulation >Adaptive Resource Provisioning Mechanism in VEEs for Improving Performance of HLA-Based Simulations
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Adaptive Resource Provisioning Mechanism in VEEs for Improving Performance of HLA-Based Simulations

机译:VEE中的自适应资源供应机制,可提高基于HLA的仿真性能

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Parallel and distributed simulations (or High-Level Architecture (HLA)-based simulations) employing optimistic synchronization allow federates to advance simulation time freely at the risk of overoptimistic executions and execution rollbacks. As a result, the simulation performance may degrade significantly due to the simulation workload imbalance among federates. In this article, we investigate the execution of parallel and distributed simulations on Cloud and data centers with Virtual Execution Environments (VEEs). In order to speed up simulation execution, an Adaptive Resource Provisioning Mechanism in Virtual Execution Environments (ArmVee) is proposed. It is composed of a performance monitor and a resource manager. The former measures federate performance transparently to the simulation application. The latter distributes available resources among federates based on the measured federate performance. Federates with different simulation workloads are thus able to advance their simulation times with comparable speeds, thus are able to avoid wasting time and resources on overoptimistic executions and execution rollbacks. ArmVee is evaluated using a real-world simulation model with various simulation workload inputs and different parameter settings. The experimental results show that ArmVee is able to speed up the simulation execution significantly. In addition, it also greatly reduces memory usage and is scalable.
机译:采用乐观同步的并行和分布式模拟(或基于高级体系结构(HLA)的模拟)使联盟可以自由地提前模拟时间,而存在执行过度乐观和执行回滚的风险。结果,由于联邦之间的模拟工作负载不平衡,模拟性能可能会大大降低。在本文中,我们将研究使用虚拟执行环境(VEE)在云和数据中心上并行和分布式仿真的执行情况。为了加快仿真执行速度,提出了虚拟执行环境中的自适应资源供应机制(ArmVee)。它由性能监视器和资源管理器组成。前者对模拟应用透明地测量联盟性能。后者根据测得的联盟性能在联盟之间分配可用资源。因此,具有不同模拟工作负载的联合会能够以相当的速度提高其模拟时间,从而能够避免在过于乐观的执行和执行回滚上浪费时间和资源。 ArmVee使用具有各种模拟工作负载输入和不同参数设置的真实模拟模型进行评估。实验结果表明ArmVee能够显着加快仿真执行速度。此外,它还大大减少了内存使用量并且具有可伸缩性。

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