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.

翻译：在调整软件配置以优化性能（如延迟或吞吐量）时，许多优化器面临的关键问题在于局部最优陷阱的存在，加之高度崎岖的配置景观与昂贵的测量成本。为缓解这些问题，近年来研究重心已从传统方法中设计更优优化器的思路，转向关注优化模型层面（称为元多目标化，即MMO）。该方法通过引入辅助性能目标与目标性能目标共同作用，帮助搜索过程跳出局部最优。尽管效果显著，MMO需要固定权重来平衡两个目标——该参数被证实至关重要，因其最优设置与其他设置之间存在巨大性能偏差。然而，面对多样化的可配置软件系统，权重“最佳平衡点”在不同场景下差异显著，若不经过耗时的试错过程则难以确定恰当设置。本文旨在通过提出权重自适应方法AdMMO来克服MMO的这一重大缺陷。我们的核心思想是在调优过程中适时自适应调整权重，从而维持非支配配置的合理比例。此外，我们设计了一种部分重复保留机制，在避免过多重复配置问题的同时，保留“优质”重复配置提供的丰富信息。在多个真实系统、目标函数及预算约束下的实验表明，在71%的案例中，AdMMO显著优于MMO及多种最先进优化器，同时普遍实现更优效率，最佳加速比达2.2倍至20倍。