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JUMMP: Job Uninterrupted Maneuverable MapReduce Platform
  • William Clay Moody, Clemson University
  • Linh B. Ngo, Clemson University
  • Edward Duffy, Clemson University
  • Amy Apon, Clemson University
Publication Date
In this paper, we present JUMMP, the Job Uninterrupted Maneuverable MapReduce Platform, an automated scheduling platform that provides a customized Hadoop environment within a batch-scheduled cluster environment. JUMMP enables an interactive pseudo-persistent MapReduce platform within the existing administrative structure of an academic high performance computing center by “jumping” between nodes with minimal administrative effort. Jumping is implemented by the synchronization of stopping and starting daemon processes on different nodes in the cluster. Our experimental evaluation shows that JUMMP can be as efficient as a persistent Hadoop cluster on dedicated computing resources, depending on the jump time. Additionally, we show that the cluster remains stable, with good performance, in the presence of jumps that occur as frequently as the average length of reduce tasks of the currently executing MapReduce job. JUMMP provides an attractive solution to academic institutions that desire to integrate Hadoop into their current computing environment within their financial, technical, and administrative constraints.

This work has been accepted for publication. Copyright is held by IEEE.

Citation Information
William Clay Moody, Linh B. Ngo, Edward Duffy and Amy Apon. "JUMMP: Job Uninterrupted Maneuverable MapReduce Platform" (2013)
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