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Hadoop-Benchmark: Rapid Prototyping and Evaluation of Self-Adaptive Behaviors in Hadoop Clusters (Artifact)

Authors Bo Zhang, Filip Krikava, Romain Rouvoy, Lionel Seinturier

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  • Filesize: 0.68 MB
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Bo Zhang
Filip Krikava
Romain Rouvoy
Lionel Seinturier

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Bo Zhang, Filip Krikava, Romain Rouvoy, and Lionel Seinturier. Hadoop-Benchmark: Rapid Prototyping and Evaluation of Self-Adaptive Behaviors in Hadoop Clusters (Artifact). In Special Issue of the 12th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS 2017). Dagstuhl Artifacts Series (DARTS), Volume 3, Issue 1, pp. 1:1-1:3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)



Arising with the popularity of Hadoop, optimizing Hadoop executions has grabbed lots of attention from research community. Many research contributions are proposed to elevate Hadoop performance, particularly in the domain of self-adaptive software systems. However, due to the complexity of Hadoop operation and the difficulty to reproduce experiments, the efforts of these Hadoop-related research are hard to be evaluated. To address this limitation, we propose a research acceleration platform for rapid prototyping and evaluation of self-adaptive behavior in Hadoop clusters. It provides an automated manner to quickly and easily provision reproducible Hadoop environments and execute acknowledged benchmarks. This platform is based on the state-of-the-art container technology that supports both distributed configurations as well as standalone single-host setups. We demonstrate the approach on a complete implementation of a concrete Hadoop self-adaptive case study.
  • Hadoop
  • Docker
  • Rapid Prototyping
  • Benchmark


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  1. Bo Zhang, Filip Křikava, Romain Rouvoy, and Lionel Seinturier. Self-Balancing Job Parallelism and Throughput in Hadoop. In 16th IFIP International Conference on Distributed Applications and Interoperable Systems (DAIS), volume 9687 of Proceedings of DAIS'16, pages 129-143, Heraklion, Crete, Greece, June 2016. Springer. URL:
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