Finding good configurations of a software system is often challenging since the number of configuration options can be large. Software engineers often make poor choices about configuration or, even worse, they usually use a sub-optimal configuration in production, which leads to inadequate performance. To assist engineers in finding the better configuration, this article introduces Flash, a sequential model-based method that sequentially explores the configuration space by reflecting on the configurations evaluated so far to determine the next best configuration to explore. Flash scales up to software systems that defeat the prior state-of-the-art model-based methods in this area. Flash runs much faster than existing methods and can solve both single-objective and multi-objective optimization problems. The central insight of this article is to use the prior knowledge of the configuration space (gained from prior runs) to choose the next promising configuration. This strategy reduces the effort (i.e., number of measurements) required to find the better configuration. We evaluate Flash using 30 scenarios based on 7 software systems to demonstrate that Flash saves effort in 100 and 80 percent of cases in single-objective and multi-objective problems respectively by up to several orders of magnitude compared to state-of-the-art techniques.

中文翻译：

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使用 FLASH 查找更快的配置

寻找软件系统的良好配置通常具有挑战性，因为配置选项的数量可能很大。软件工程师经常在配置上做出糟糕的选择，更糟糕的是，他们通常在生产中使用次优配置，从而导致性能不足。为了帮助工程师找到更好的配置，本文介绍了 Flash，这是一种基于顺序模型的方法，它通过反映迄今为止评估的配置来顺序探索配置空间，以确定要探索的下一个最佳配置。Flash 扩展到软件系统，在该领域击败了先前最先进的基于模型的方法。Flash 运行速度比现有方法快得多，并且可以解决单目标和多目标优化问题。本文的核心见解是使用配置空间的先验知识（从先前运行中获得）来选择下一个有希望的配置。该策略减少了寻找更好配置所需的工作量（即测量次数）。我们使用基于 7 个软件系统的 30 个场景对 Flash 进行评估，以证明 Flash 在单目标和多目标问题的案例中分别节省了 100% 和 80% 的工作量，与最先进的技术相比最多可节省几个数量级技术。