Software configuration tuning is essential for optimizing a given performance objective (e.g., minimizing latency). Yet, due to the software's intrinsically complex configuration landscape and expensive measurement, there has been a rather mild success, particularly in preventing the search from being trapped in local optima. To address this issue, in this paper we take a different perspective. Instead of focusing on improving the optimizer, we work on the level of optimization model and propose a meta multi-objectivization (MMO) model that considers an auxiliary performance objective (e.g., throughput in addition to latency). What makes this model unique is that we do not optimize the auxiliary performance objective, but rather use it to make similarly-performing while different configurations less comparable (i.e. Pareto nondominated to each other), thus preventing the search from being trapped in local optima. Importantly, we show how to effectively use the MMO model without worrying about its weight -- the only yet highly sensitive parameter that can affect its effectiveness. Experiments on 22 cases from 11 real-world software systems/environments confirm that our MMO model with the new normalization performs better than its state-of-the-art single-objective counterparts on 82% cases while achieving up to 2.09x speedup. For 67% of the cases, the new normalization also enables the MMO model to outperform the instance when using it with the normalization used in our prior FSE work under pre-tuned best weights, saving a great amount of resources which would be otherwise necessary to find a good weight. We also demonstrate that the MMO model with the new normalization can consolidate Flash, a recent model-based tuning tool, on 68% of the cases with 1.22x speedup in general.
翻译:软件配置调整对于优化给定的绩效目标至关重要(例如,降低正值值 )。然而,由于软件本身的复杂配置面貌和昂贵的测量,在防止搜索被困在本地optima 中取得了相当温和的成功,特别是在防止搜索被困在本地Popima 中。为了解决这一问题,我们在本文中采取了不同的观点。我们不注重优化优化,而是在优化模型的水平上开展工作,并提出一个考虑辅助性绩效目标(例如,降低平流率)的元多客观化模型(MMO)模式。由于该模型的独特性,我们之所以之所以之所以能够使该模型具有独特性,是因为我们没有优化辅助性功能的复杂配置景观和昂贵的测量,是因为我们没有优化辅助性绩效目标,而是取得了相当的成功,特别是在防止搜索被困在本地Popima 中。我们展示了如何有效地使用MMO模型而不担心其份量 -- -- 我们只有高度敏感的通用参数才能影响其有效性。从11个实体软件系统/环境的22个案例中进行实验,从11个实际软件系统/环境中也展示了演示。 必要的我们MMO模型在新的正常模式中展示了一个新的速度模型中,在一个新的正值模型中,一个新的正值模型中,在使用一个新的正标值模型中,在使用一个比正值的正值中,一个比值的正值的正值的正值的正值的正值的正值的正值中,在使用一个正值中,在使用一个正值的正值模型中,一个比值的正值模型中,在比值的正值的正值的正值的正值的正值的正值的正值模型中,在比值模型中,在比值中,在比值的正值的正值的正值的正值的正值的正值的正值的正值中,一个正值模型中,在比值中,在比值的正值的正值的正值的正值的正值的正值的正值的正值的正值的正值中,在比值的正值中,在比值中,在比值的正值的正值的正值中,在比值的正值的正值的正值的正值的正值的正值的正值的正值