适配多目标化的软件配置调优
Adapting Multi-objectivized Software Configuration Tuning
AI总结：
针对元多目标化软件配置调优中固定权重依赖试错的问题，提出 AdMMO 自适应权重方法及部分重复保留机制，在 71% 场景中优于现有方法，最高加速 2.2–20 倍。
摘要：
在调优软件配置以提升性能（例如延迟或吞吐量）时，许多优化器面临的一个重要问题是局部最优陷阱，此外还有高度崎岖的配置景观和昂贵的测量。为缓解这些问题，近期的一项工作转向关注优化模型层面（称为元多目标化或 MMO），而非像传统方法那样设计更好的优化器。其做法是使用一个辅助性能目标，并与目标性能目标一起，帮助搜索跳出局部最优。尽管有效，但 MMO 需要一个固定权重来平衡两个目标——该参数已被发现至关重要，因为最佳设置与其他设置之间存在巨大的性能偏差。然而，鉴于可配置软件系统的多样性，权重的“最佳点”在不同情况下可能显著变化，若不进行耗时的试错，就不可能找到正确设置。本文中，我们试图通过提出一种名为 AdMMO 的权重自适应方法来克服 MMO 的这一显著缺陷。我们的核心思想是在调优过程中的适当时机自适应地调整权重，从而维持良好比例的非支配配置。此外，我们设计了一种部分重复保留机制，以处理重复配置过多的问题，同时不丢失“好的”重复项所提供的丰富信息。在多个真实系统、目标和预算上的实验表明，在 71% 的情况下，AdMMO 显著优于 MMO 以及一系列广泛的最先进优化器，同时总体上实现了更好的效率，最佳加速比在 2.2 倍到 20 倍之间。
英文摘要：
When tuning software configuration for better performance (e.g., latency or throughput), an important issue that many optimizers face is the presence of local optimum traps, compounded by a highly rugged configuration landscape and expensive measurements. To mitigate these issues, a recent effort has shifted to focus on the level of optimization model (called meta multi-objectivization or MMO) instead of designing better optimizers as in traditional methods. This is done by using an auxiliary performance objective, together with the target performance objective, to help the search jump out of local optima. While effective, MMO needs a fixed weight to balance the two objectives-a parameter that has been found to be crucial as there is a large deviation of the performance between the best and the other settings. However, given the variety of configurable software systems, the "sweet spot" of the weight can vary dramatically in different cases and it is not possible to find the right setting without time-consuming trial and error. In this paper, we seek to overcome this significant shortcoming of MMO by proposing a weight adaptation method, dubbed AdMMO. Our key idea is to adaptively adjust the weight at the right time during tuning, such that a good proportion of the nondominated configurations can be maintained. Moreover, we design a partial duplicate retention mechanism to handle the issue of too many duplicate configurations without losing the rich information provided by the "good" duplicates. Experiments on several real-world systems, objectives, and budgets show that, for 71% of the cases, AdMMO is considerably superior to MMO and a wide range of state-of-the-art optimizers while achieving generally better efficiency with the best speedup between 2.2x and 20x.
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。
