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首页> 外文期刊>Journal of Parallel and Distributed Computing >Dynamic balancing of communication and computation load for HLA-based simulations on large-scale distributed systems
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Dynamic balancing of communication and computation load for HLA-based simulations on large-scale distributed systems

机译：大型分布式系统上基于HLA的仿真的通信和计算负载的动态平衡

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摘要

Dynamic balancing of computation and communication load is vital for the execution stability and performance of distributed, parallel simulations deployed on the shared, unreliable resources of large-scale environments. High Level Architecture (HLA) based simulations can experience a decrease in performance due to imbalances that are produced initially and/or during run time. These imbalances are generated by the dynamic load changes of distributed simulations or by unknown, non-managed background processes resulting from the non-dedication of shared resources. Due to the dynamic execution characteristics of elements that compose distributed applications, the computational load and interaction dependencies of each simulation entity change during run time. These dynamic changes lead to an irregular load and communication distribution, which increases overhead of resources and latencies. A static partitioning of load is limited to deterministic applications and is incapable of predicting the dynamic changes caused by distributed applications or by external background processes. Therefore, a scheme for balancing the communication and computational load during the execution of distributed simulations is devised in a scalable hierarchical architecture. The proposed balancing system employs local and cluster monitoring mechanisms in order to observe the distributed load changes and identify imbalances, repartitioning policies to determine a distribution of load and minimize imbalances. A migration technique is also employed by this proposed balancing system to perform reliable and low-latency load transfers. Such a system successfully improves the use of shared resources and increases distributed simulations' performance by minimizing communication latencies and partitioning the load evenly. Experiments and comparative analyses were conducted in order to identify the gains that the proposed balancing scheme provides to large-scale distributed simulations.
机译：动态平衡计算和通信负载对于在大规模环境中共享，不可靠的资源上部署的分布式并行仿真的执行稳定性和性能至关重要。由于最初和/或在运行时产生的不平衡，基于高层体系结构（HLA）的模拟可能会导致性能下降。这些不平衡是由分布式模拟的动态负载变化或由不专用共享资源导致的未知，不受管理的后台进程产生的。由于组成分布式应用程序的元素的动态执行特性，每个模拟实体的计算负载和交互依赖性在运行时会发生变化。这些动态变化导致不规则的负载和通信分配，从而增加了资源开销和延迟。负载的静态划分仅限于确定性应用程序，并且无法预测由分布式应用程序或外部后台进程引起的动态变化。因此，在可扩展的分层体系结构中设计了一种方案，用于在执行分布式仿真期间平衡通信和计算负载。提出的平衡系统采用本地和群集监视机制，以观察分布式负载变化并识别不平衡，重新划分策略以确定负载分布并最大程度地减少不平衡。此提议的平衡系统还采用了一种迁移技术来执行可靠且低延迟的负载转移。这样的系统通过最小化通信延迟和平均分配负载，成功改善了共享资源的使用并提高了分布式仿真的性能。为了确定所提出的平衡方案为大规模分布式仿真提供的收益，进行了实验和比较分析。

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