AutoTune: Optimizing Execution Concurrency and Resource Usage in MapReduce Workflows

Provided by: University of Peloponnese
Topic: Big Data
Format: PDF
An increasing number of MapReduce applications are written using high-level SQL-like abstractions on top of MapReduce engines. Such programs are translated into MapReduce workflows where the output of one job becomes the input of the next job in a workflow. A user must specify the number of reduce tasks for each MapReduce job in a workflow. The reduce task setting may have a significant impact on the execution concurrency, processing efficiency, and the completion time of the workflow.

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