Heterogeneity-aware scheduler for stream processing frameworks
by Marek Rychlý; Petr Škoda; Pavel Smrž
International Journal of Big Data Intelligence (IJBDI), Vol. 2, No. 2, 2015

Abstract: This article discusses problems and decisions related to scheduling of stream processing applications in heterogeneous clusters. An overview of the current state of the art of the stream processing on heterogeneous clusters with a focus on resource allocation and scheduling is presented first. Then, common scheduling approaches of various stream processing frameworks are discussed and their limited applicability in the heterogeneous environment is demonstrated on a simple stream application. Finally, the article presents a novel heterogeneity-aware scheduler for the stream processing frameworks based on design-time knowledge as well as benchmarking techniques. It is shown that the scheduler overcomes alternatives in resource-aware deployment over cluster nodes and thus it leads to a better utilisation of the clusters.

Online publication date: Sat, 09-May-2015

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