On-the-fly Task Execution for Speeding Up Pipelined MapReduce - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2012

On-the-fly Task Execution for Speeding Up Pipelined MapReduce


The MapReduce programming model is widely acclaimed as a key solution to designing data-intensive applications. However, many of the computations that fit this model cannot be expressed as a single MapReduce execution, but require a more complex design. Such applications consisting of multiple jobs chained into a long-running execution are called pipeline MapReduce applications. Standard MapReduce frameworks are not optimized for the specific requirements of pipeline applications, yielding performance issues. In order to optimize the execution on pipelined MapReduce, we propose a mechanism for creating map tasks along the pipeline, as soon as their input data becomes available. We implemented our approach in the Hadoop MapReduce framework. The benefits of our dynamic task scheduling are twofold: reducing job-completion time and increasing cluster utilization by involving more resources in the computation. Experimental evaluation performed on the Grid'5000 testbed, shows that our approach delivers performance gains between 9% and 32%.
Fichier principal
Vignette du fichier
main.pdf (193.25 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-00706844 , version 1 (11-06-2012)


  • HAL Id : hal-00706844 , version 1


Diana Moise, Gabriel Antoniu, Luc Bougé. On-the-fly Task Execution for Speeding Up Pipelined MapReduce. Euro-Par - 18th International European Conference on Parallel and Distributed Computing - 2012, Aug 2012, Rhodes Island, Greece. ⟨hal-00706844⟩
393 View
260 Download


Gmail Facebook X LinkedIn More