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H-word: Supporting job scheduling in Hadoop with workload-driven data redistribution

par Jovanovic, Petar ;Romero, Oscar;Calders, Toon ;Abelló, Alberto
Référence Lecture notes in computer science, 9809, page (306-320)
Publication Publié, 2016

Article révisé par les pairs

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Résumé :

Today’s distributed data processing systems typically follow a query shipping approach and exploit data locality for reducing network traffic. In such systems the distribution of data over the cluster resources plays a significant role, and when skewed, it can harm the performance of executing applications. In this paper, we address the challenges of automatically adapting the distribution of data in a cluster to the workload imposed by the input applications. We propose a generic algorithm, named H-WorD, which, based on the estimated workload over resources, suggests alternative execution scenarios of tasks, and hence identifies required transfers of input data a priori, for timely bringing data close to the execution. We exemplify our algorithm in the context of MapReduce jobs in a Hadoop ecosystem. Finally, we evaluate our approach and demonstrate the performance gains of automatic data redistribution.