网格中的动态资源可用性

A. Iosup, M. Jan, Omer Ozan Sonmez, D. Epema
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引用次数: 115

摘要

目前部署的网格聚集了成千上万的计算和存储资源,以造福于一个庞大的科学家群体。然而,大规模、广泛的地理分布,以及有时合法的资源所有者决定将能力交付给其他地方,引发了严重的资源可用性问题。人们对网格资源可用性的特征以及资源不可用对网格性能的影响知之甚少。在这项工作中,我们在解决这一双重信息缺乏方面迈出了第一步。首先,我们分析了Grid'5000的长期可用性跟踪并评估了资源可用性特征,Grid'5000是一个超过2500个处理器的实验网格环境。考虑可用性时,所研究的跟踪的平均利用率提高了近5%。在分析结果的基础上,进一步提出了网格资源可用性模型。我们的分析和建模结果表明,网格计算资源以很高的速率变得不可用,这对网格执行长时间作业的能力产生了负面影响。其次,通过基于跟踪的仿真,我们证明了资源可用性会对网格系统的性能产生严重影响。这一步的结果表明,当考虑可用性时,网格系统的性能可以提高,并且人工管理可用性更改信息导致的作业失败比自动监控解决方案多10-15倍,即使对于利用率较低的系统也是如此。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On the dynamic resource availability in grids
Currently deployed grids gather together thousands of computational and storage resources for the benefit of a large community of scientists. However, the large scale, the wide geographical spread, and at times the decision of the rightful resource owners to commit the capacity elsewhere, raises serious resource availability issues. Little is known about the characteristics of the grid resource availability, and of the impact of resource unavailability on the performance of grids. In this work, we make first steps in addressing this twofold lack of information. First, we analyze a long-term availability trace and assess the resource availability characteristics of Grid'5000, an experimental grid environment of over 2,500 processors. The average utilization for the studied trace is increased by almost 5%, when availability is considered. Based on the results of the analysis, we further propose a model for grid resource availability. Our analysis and modeling results show that grid computational resources become unavailable at a high rate, negatively affecting the ability of grids to execute long jobs. Second, through trace-based simulation, we show evidence that resource availability can have a severe impact on the performance of the grid systems. The results of this step show evidence that the performance of a grid system can rise when availability is taken into consideration, and that human administration of availability change information results in 10-15 times more job failures than for an automated monitoring solution, even for a lowly utilized system.
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