Unicorn: Reasoning about Configurable System Performance through the lens of Causality. (arXiv:2201.08413v1 [cs.LG])

Web: http://arxiv.org/abs/2201.08413

Modern computer systems are highly configurable, with the variability space
sometimes larger than the number of atoms in the universe. Understanding and
reasoning about the performance behavior of highly configurable systems, due to
a vast variability space, is challenging. State-of-the-art methods for
performance modeling and analyses rely on predictive machine learning models,
therefore, they become (i) unreliable in unseen environments (e.g., different
hardware, workloads), and (ii) produce incorrect explanations. To this end, we
propose a new method, called Unicorn, which …

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