Enhancing Load Balancing Efficiency Based on Migration Delay for Large-Scale Distributed Simulations
Enhancing Load Balancing Efficiency Based on Migration Delay for Large-Scale Distributed Simulations
复制标题
基于迁移延迟的大规模分布式仿真负载均衡效率提升
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
A. Boukerche
中科院分区：
文献类型：
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作者：
Turki G. Alghamdi;R. E. Grande;A. Boukerche
负载管理对于运行在共享资源上的分布式仿真来说是一个必不可少的重要因素，因为负载不平衡会导致相当大的性能损失。此功能对于基于高层体系结构（HLA）的仿真至关重要，因为HLA框架不提供管理资源或帮助检测可能直接导致性能下降的负载不平衡的能力。为了提供一种适用于大规模环境的高效负载平衡方案，设计了一个迁移感知的动态平衡系统。该系统在估计成本和收益方面存在一些限制，因此我们提出了对现有负载平衡系统的增强，从而提高了生成联邦成员迁移的准确性。该方案的目的是通过分析共享资源上的负载来精确估计迁移延迟和增益，防止对仿真执行时间昂贵的迁移问题。在性能分析，所提出的决策分析方案已显示出减少迁移的数量，从而减少执行时间的改进。
Load management is an essential and important factor for distributed simulations running on shared resources due to load imbalances that can caused considerable performance loss. This feature is essential for High Level Architecture (HLA)-based simulations since the HLA framework does not present the ability to manage resources or help detect load imbalances that could directly cause decrease of performance. A migration-aware dynamic balancing system has been designed for HLA simulations to offer an efficient load-balancing scheme that works in large-scale environments. This system presents some limitations on estimating costs and benefits, so we propose an enhancement to this existing load balancing system, which improves the accuracy of generating federate migrations. The proposed scheme aims to precisely estimate the migration delay and gain by analyzing the load on shared resources, preventing the issuing of migrations costly towards simulation execution time. Upon a performance analysis, the proposed decision-making analysis scheme has shown an improvement on decreasing the number of migrations and consequently decreasing execution time.