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

by Bo Zhang, Filip Krikava, Romain Rouvoy, and Lionel Seinturier


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.


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