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.