US Patent 10257275 Tuning software execution environments using Bayesian models

An optimizer for a software execution environment determines an objective function and permitted settings for various tunable parameters of the environment. To represent the execution environment, the optimizer generates a Bayesian optimization model employing Gaussian process priors. The optimizer implements a plurality of iterations of execution of the model, interleaved with observation collection intervals. During a given observation collection interval, tunable parameter settings suggested by the previous model execution iteration are used in the execution environment, and the observations collected during the interval are used as inputs for the next model execution iteration. When an optimization goal is attained, the tunable settings that led to achieving the goal are stored.