Quincy: Fair Scheduling for Distributed Computing Clusters

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Provided by: Microsoft
Topic: Big Data
Format: PDF
In this paper, the authors address the problem of scheduling concurrent jobs on clusters where application data is stored on the computing nodes. This setting, in which scheduling computations close to their data is crucial for performance, is increasingly common and arises in systems such as MapReduce, Hadoop, and Dryad as well as many grid-computing environments. The authors argue that data intensive computation benefits from a fine-grain resource sharing model that differs from the coarser semi-static resource allocations implemented by most existing cluster computing architectures.
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