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Software Design and Implementation for MapReduce across Distributed Data Centers |
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PP: 85-90 |
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Author(s) |
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Lizhe Wang,
Jie Tao,
Yan Ma,
Samee U. Khan,
Joanna Kołodziej,
Dan Chen,
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Abstract |
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| Recently, the computational requirements for large-scale data-intensive analysis of scientific data have grown significantly.
In High Energy Physics (HEP) for example, the Large Hadron Collider (LHC) produced 13 petabytes of data in 2010. This huge amount
of data are processed on more than 140 computing centers distributed across 34 countries. The MapReduce paradigm has emerged
as a highly successful programming model for large-scale data-intensive computing applications. However, current MapReduce
implementations are developed to operate on single cluster environments and cannot be leveraged for large-scale distributed data
processing across multiple clusters. On the other hand, workflow systems are used for distributed data processing across data centers. It
has been reported that the workflow paradigm has some limitations for distributed data processing, such as reliability and efficiency. In
this paper, we present the design and implementation of G-Hadoop, a MapReduce framework that aims to enable large-scale distributed
computing across multiple clusters. G-Hadoop uses the Gfarm file system as an underlying file system and executes MapReduce tasks
across distributed clusters. |
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