MapReduce Makes Further Inroads in Academia by Ian Armas Foster.
From the post:
Most conversations about Hadoop and MapReduce tend to filter in from enterprise quarters, but if the recent uptick in scholarly articles extolling its benefit for scientific and technical computing applications is any indication, the research world might have found its next open source darling.
Of course, it’s not just about making use of the approach—for many researchers, it’s about expanding, refining and tweaking the tool to make it suitable for new, heavy-hitting class of applications. As a result, research to improve MapReduce’s functionality and efficiency flourishes, which could eventually provide some great trickle-down technology for the business users as well.
As one case among an increasing number, researchers Marcelo Neves, Tiago Ferreto, and Cesar De Rose of PUCRS in Brazil are working to extend the capabilities of MapReduce. Their approach to MapReduce sought to tackle one of the more complex issues for MapReduce on high performance computing hardware. In this case, the mighty scheduling problem was the target.
The team recently proposed a new algorithm that would enhance MapReduce’s work rate and job scheduling called MapReduce Job Adaptor. Neves et al presented their algorithm in a recent paper.
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I am not sure how (or if) it will be documented, but users of MapReduce should watch for how their analysis of a problem changes, based on the anticipated use of MapReduce.
Some academic is going to write the history of MapReduce on one or more problems. Could be you.