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首页> 外文会议>2017 XLIII Latin American Computer Conference >A performance comparison of data-aware heuristics for scheduling jobs in mobile grids
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A performance comparison of data-aware heuristics for scheduling jobs in mobile grids

机译：用于移动网格中的作业调度的数据感知启发式方法的性能比较

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摘要

Given mobile devices ubiquity and capabilities, some researchers now consider them as resource providers of distributed environments called mobile Grids for running resource intensive software. Therefore, job scheduling has to deal with device singularities, such as energy constraints, mobility and unstable connectivity. Many existing schedulers consider at least one of these aspects, but their applicability strongly depends on information that is unavailable or difficult to estimate accurately, like job execution time. Other efforts do not assume knowing job CPU requirements but ignore energy consumption due to data transfer operations, which is not realistic for data-intensive applications. This work, on the contrary, considers the last as non negligible and known by the scheduler. Under these assumptions, we conduct a performance study of several traditional scheduling heuristics adapted to this environment, which are applied with the known information of jobs but evaluated along with job information unknown to the scheduler. Experiments are performed via a simulation software that employs hardware profiles derived from real mobile devices. Our goal is to contribute to better understand both the capabilities and limitations of this kind of schedulers in the incipient area of mobile Grids.
机译：考虑到移动设备的普遍性和功能，现在一些研究人员将其视为运行资源密集型软件的分布式环境的资源提供者，称为移动网格。因此，作业调度必须处理设备的奇异性，例如能量限制，移动性和不稳定的连接性。许多现有的调度程序至少考虑了这些方面之一，但是它们的适用性在很大程度上取决于无法获得或难以准确估计的信息，例如作业执行时间。其他工作并不假设知道作业的CPU要求，而是忽略了由于数据传输操作而造成的能耗，这对于数据密集型应用程序是不现实的。相反，这项工作认为最后一项是不可忽略的，调度程序也知道它。在这些假设下，我们对几种适用于此环境的传统调度启发式方法进行了性能研究，将其与作业的已知信息一起应用，但与调度程序未知的作业信息一起进行评估。实验是通过模拟软件执行的，该软件采用了源自真实移动设备的硬件配置文件。我们的目标是有助于更好地了解此类调度程序在移动网格初期领域中的功能和局限性。

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