使用FLASH查找更快的搜索
Finding Faster Configurations using FLASH
摘要：
由于配置选项的数量可能很大，因此为软件系统找到良好的配置通常具有挑战性。软件工程师经常对配置做出糟糕的选择，甚至更糟的是，他们通常在生产中使用次优配置，这导致性能不足。为了帮助工程师在寻找（近）最佳配置，本文介绍了闪存，顺序模型为基础的方法，顺序探索配置空间，通过反映到目前为止评估的配置，以确定下一个最佳配置探索。FLASH扩展到软件系统，击败了该领域现有的基于模型的方法。FLASH运行速度比现有方法快得多，可以解决单目标和多目标优化问题。本文的核心观点是使用先验知识（从以前的运行中获得）来选择下一个有希望的配置。这种策略减少了工作量（即，测量次数），以找到（接近）最佳配置。我们评估FLASH使用30个场景的基础上7个软件系统，以证明FLASH节省了100%和80%的情况下，在单目标和多目标的问题分别高达几个数量级的最先进的技术相比，努力。
英文摘要：
Finding good configurations for 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 (near) optimal configuration, this paper introduces FLASH, a sequential model-based method, which 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 paper is to use the prior knowledge (gained from prior runs) to choose the next promising configuration. This strategy reduces the effort (i.e., number of measurements) required to find the (near) optimal configuration. We evaluate FLASH using 30 scenarios based on 7 software systems to demonstrate that FLASH saves effort in 100% and 80% of cases in single-objective and multi-objective problems respectively by up to several orders of magnitude compared to the state of the art techniques.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
