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Data Replication and Power Consumption in Data Grids

机译:数据网格中的数据复制和功耗

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摘要

While data grids can provide the ability to solve large-scale applications which require the processing of large amounts of data, they have been recognized as extremely energy inefficient. Computing elements can be located far away from the data storage elements. A common solution to improve availability and file access time in such environments is to replicate the data, resulting in the creation of copies of data files at many different sites. The energy efficiency of the data centers storing this data is one of the biggest issues in data intensive computing. Since power is needed to transmit, store and cool the data, we propose to minimize the amount of data transmitted and stored by utilizing smart replication strategies that are data aware. In this paper we present a new data replication approach, called the sliding window replica strategy (SWIN), that is not only data aware, but is also energy efficient. We measure the performance of SWIN and existing replica strategies on our Sage green cluster to study the power consumption of the strategies. Results from this study have implications beyond our cluster to the management of data in clouds.
机译:尽管数据网格可以提供解决需要处理大量数据的大规模应用程序的能力,但它们已被认为具有极低的能源效率。计算元素可以位于远离数据存储元素的位置。在此类环境中提高可用性和文件访问时间的常见解决方案是复制数据,从而在许多不同的站点上创建数据文件的副本。存储此数据的数据中心的能源效率是数据密集型计算中的最大问题之一。由于需要电源来传输,存储和冷却数据,因此我们建议通过利用具有数据感知能力的智能复制策略来最大程度地减少传输和存储的数据量。在本文中,我们提出了一种新的数据复制方法,称为滑动窗口复制策略(SWIN),它不仅具有数据感知能力,而且还具有能源效率。我们在Sage绿色集群上测量SWIN和现有复制策略的性能,以研究这些策略的功耗。这项研究的结果对我们云计算中的数据管理不仅仅具有我们的集群意义。

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