Poster: Searching for High-Performing Software Configurations with Metaheuristic Algorithms

Modern systems often have complex configuration spaces. Research has shown that people often just use default settings. This practice leaves significant performance potential unrealized. In this work, we propose an approach that uses metaheuristic search algorithms to explore the configuration space of Hadoop for high-performing configurations. We present results of a set of experiments to show that our approach can find configurations that perform significantly better than defaults. We tested two metaheuristic search algorithms—coordinate descent and genetic algorithms—for three common MapReduce programs—Wordcount, Sort, and Terasort—for a total of six experiments. Our results suggest that metaheuristic search can find configurations cost-effectively that perform significantly better than baseline default configurations.

现代系统通常具有复杂的配置空间。执行配置。我们提出了一组实验的结果默认情况下，我们测试了两个元启发式搜索算法（坐标下降和遗传算法），用于三个常见的MAPREDUCE程序 - WordCount，Sort和Terasort，我们的结果总共进行了六个实验。明显好于基线默认配置。