Many software systems can be tuned for multiple objectives (e.g., faster runtime, less required memory, less network traffic or energy consumption, etc.). Optimizers built for different objectives suffer from "model disagreement"; i.e., they have different (or even opposite) insights and tactics on how to optimize a system. Model disagreement is rampant (at least for configuration problems). Yet prior to this paper, it has barely been explored. This paper shows that model disagreement can be mitigated via VEER, a one-dimensional approximation to the N-objective space. Since it is exploring a simpler goal space, VEER runs very fast (for eleven configuration problems). Even for our largest problem (with tens of thousands of possible configurations), VEER finds as good or better optimizations with zero model disagreements, three orders of magnitude faster (since its one-dimensional output no longer needs the sorting procedure). Based on the above, we recommend VEER as a very fast method to solve complex configuration problems, while at the same time avoiding model disagreement.

翻译：许多软件系统可针对多个目标进行调优（例如，更快的运行时间、更少的内存占用、更少的网络流量或能耗等）。为不同目标构建的优化器存在"模型分歧"问题，即它们对如何优化系统持有不同（甚至相反）的见解与策略。模型分歧现象普遍存在（至少对于配置问题而言），但在本文之前鲜有探索。本文证明，通过VEER（一种N目标空间的一维近似方法）可以缓解模型分歧。由于探索更简单的目标空间，VEER在十一个配置问题上运行极快。即使面对最大的问题（包含数万种可能配置），VEER能在零模型分歧的情况下找到同等或更优的优化方案，运行速度快三个数量级（因其一维输出不再需要排序过程）。基于上述结果，我们推荐VEER作为解决复杂配置问题的极快速方法，同时能够避免模型分歧。