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A heterogeneity-aware approach to load balancing of computational tasks: a theoretical and simulation study

机译：一种用于计算任务负载均衡的异构感知方法：理论和仿真研究

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摘要

One of the distinct characteristics of computing platforms shared by multiple users such as a cluster and a computational grid is heterogeneity on each computer and/or among computers. Temporal heterogeneity refers to variation, along the time dimension, of computing power available for a task on a computer, and spatial heterogeneity represents the variation among computers. In minimizing the average parallel execution time of a target task on a spatially heterogeneous computing system, it is not optimal to distribute the target task linearly proportional to the average computing powers available on computers. In this paper, effects of the temporal and spatial heterogeneity on performance of a target task have been analyzed in terms of the mean and standard deviation of parallel execution time. Based on the analysis results, an approach to load balancing for minimizing the average parallel execution time of a target task is described. The proposed approach whose validity has been verified through simulation considers temporal and spatial heterogeneities in addition to the average computing power on each computer.
机译：由多个用户（例如集群和计算网格）共享的计算平台的显着特征之一是每台计算机和/或计算机之间的异构性。时间异质性是指沿着时间维度的可用于计算机上的任务的计算能力的变化，而空间异质性表示计算机之间的变化。在使目标任务在空间异构计算系统上的平均并行执行时间最小化的过程中，将目标任务与计算机上可用的平均计算能力成线性比例地分配并不是最优的。本文根据并行执行时间的均值和标准差分析了时间和空间异质性对目标任务性能的影响。基于分析结果，描述了一种用于最小化目标任务的平均并行执行时间的负载平衡方法。通过仿真验证其有效性的拟议方法除考虑每台计算机的平均计算能力外，还考虑了时间和空间异质性。

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