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MapReduce has been widely used as a Big Data processing platform. As it gets popular, its scheduling becomes increasingly important. In particular, since many MapReduce applications require real-time data processing, scheduling real time applications in MapReduce environments has become a significant problem. In this paper, we create a novel real-time scheduler for MapReduce, which overcomes the deficiencies...
MapReduce is a programming model and an associated implementation for processing and generating large data sets. Hadoop is an open-source implementation of Map Reduce, enjoying wide adoption, and is used not only for batch jobs but also for short jobs where low response time is critical. However, Hadoop's performance is currently limited by its default task scheduler, which implicitly assumes that...
MapReduce is a powerful data processing platform for commercial and academic applications. In this paper, we build a novel Hadoop MapReduce framework executed on the Open Science Grid which spans multiple institutions across the United States -- Hadoop On the Grid (HOG). It is different from previous MapReduce platforms that run on dedicated environments like clusters or clouds. HOG provides a free,...
MapReduce is a powerful platform for large-scale data processing. To achieve good performance, a MapReduce scheduler must avoid unnecessary data transmission by enhancing the data locality (placing tasks on nodes that contain their input data). This paper develops a new MapReduce scheduling technique to enhance map task's data locality. We have integrated this technique into Hadoop default FIFO scheduler...
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