解锁软件配置景观的秘密——崎岖性、可达性、可逃逸性与可迁移性
Unlocking the Secrets of Software Configuration Landscapes-Ruggedness, Accessibility, Escapability, and Transferability
AI总结：
本文对三个真实软件系统在32个工作负载上的8600万个配置进行首次适应度景观分析，揭示配置景观崎岖但顶级局部最优可达、较差局部最优可逃逸且跨负载结构相似，为配置调优的元启发式设计提供基础。
摘要：
现代软件系统通常高度可配置，以满足不同利益相关者的多样化需求。理解配置与期望性能属性之间的映射关系，在提升底层系统的可控性和调优能力方面发挥着基础性作用，然而由于系统的黑盒特性以及巨大的组合配置空间，这一映射长期以来一直是知识的黑洞。本文利用来自三个真实系统在32个工作负载上评估的8600万个配置，对可配置软件系统进行了同类首次的适应度景观分析（FLA）。通过全面的FLA方法，我们首次表明：i）软件配置景观相当崎岖，存在大量分散的局部最优解；ii）尽管如此，顶级局部最优解具有高度可达性，其特征是显著更大的吸引盆；iii）大多数较差的局部最优解可通过简单扰动逃脱；iv）同一系统在不同工作负载下的景观具有结构相似性，可利用此相似性加速启发式搜索。我们的结果还为设计针对配置调优的定制元启发式算法提供了宝贵见解；我们的FLA框架连同收集的数据，为未来这一方向的研究奠定了坚实基础。
英文摘要：
Modern software systems are often highly configurable to tailor varied requirements from diverse stakeholders. Understanding the mapping between configurations and the desired performance attributes plays a fundamental role in advancing the controllability and tuning of the underlying system, yet has long been a dark hole of knowledge due to their black-box nature and the enormous combinatorial configuration space. In this paper, using $86$M evaluated configurations from three real-world systems on $32$ running workloads, we conducted one of its kind fitness landscape analysis (FLA) for configurable software systems. With comprehensive FLA methods, we for the first time show that: $i)$ the software configuration landscapes are fairly rugged, with numerous scattered local optima; $ii)$ nevertheless, the top local optima are highly accessible, featuring significantly larger basins of attraction; $iii)$ most inferior local optima are escapable with simple perturbations; $iv)$ landscapes of the same system with different workloads share structural similarities, which can be exploited to expedite heuristic search. Our results also provide valuable insights on the design of tailored meta-heuristics for configuration tuning; our FLA framework along with the collected data, build solid foundation for future research in this direction.
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。
