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PVLDB
2010

HaLoop: Efficient Iterative Data Processing on Large Clusters

13 years 4 months ago
HaLoop: Efficient Iterative Data Processing on Large Clusters
The growing demand for large-scale data mining and data analysis applications has led both industry and academia to design new types of highly scalable data-intensive computing platforms. MapReduce and Dryad are two popular platforms in which the dataflow takes the form of a directed acyclic graph of operators. These platforms lack built-in support for iterative programs, which arise naturally in many applications including data mining, web ranking, graph analysis, model fitting, and so on. This paper presents HaLoop, a modified version of the Hadoop MapReduce framework that is designed to serve these applications. HaLoop not only extends MapReduce with programming support for iterative applications, it also dramatically improves their efficiency by making the task scheduler loop-aware and by adding various caching mechanisms. We evaluated HaLoop on real queries and real datasets. Compared with Hadoop, on average, HaLoop reduces
Yingyi Bu, Bill Howe, Magdalena Balazinska, Michae
Added 20 May 2011
Updated 20 May 2011
Type Journal
Year 2010
Where PVLDB
Authors Yingyi Bu, Bill Howe, Magdalena Balazinska, Michael Ernst
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