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.
Moody, William Clay; Ngo, Linh B.; Duffy, Edward; and Apon, Amy, "JUMMP: Job Uninterrupted Maneuverable MapReduce Platform" (2013). Presentations. Paper 2.