HYPPO: A Hybrid, Piecewise Polynomial Modeling Technique for Non-Smooth Surfaces

Abstract

The number and diversity of tunable parameters in applications makes predicting settings that achieve optimal performance challenging. Complicating matters is the fact that resources are increasingly shared among computational tasks (for example, in cloud environments). Choosing any setting that yields near-optimal performance runs the risk of overusing shared resources. Building accurate models that capture the complicated interplay of parameters is crucial in order to maximize performance with minimal resource impact. Traditional techniques tend to fall short when modeling performance. One reason is that performance surfaces are often irregular but most traditional techniques are designed to produce smooth models. In this paper we introduce a hybrid modeling technique that combines the strengths of surrogate-based modeling (SBM) and k nearest-neighbor regression (kNN) into a single method called HYPPO. The hybrid method is a piecewise polynomial model composed of many small, local models. We demonstrate that HYPPO significantly improves overall prediction accuracy compared with SBM and kNN.