Tuning software execution environments using Bayesian models

A method, comprising:

• performing, by a network-accessible optimization tool implemented using one or more computing devices:
• identifying, as a candidate for performance optimization, an execution environment comprising one or more software components, wherein the execution environment has a plurality of tunable parameters;
• receiving, from a client, (a) a definition of an objective function that specifies a non-linear transformation of one or more execution results produced by the one or more software components to produce as output one or more objective function values to be optimized and (b) a respective plurality of permissible settings for at least a first tunable parameter and a second tunable parameter of the plurality of tunable parameters;
• generating, with respect to the execution environment, a Bayesian model employing a Gaussian process, wherein input to the Bayesian model includes the one or more objective function values and output of the Bayesian model includes settings for at least the first and second tunable parameters;
• implementing a plurality of iterations of execution of the Bayesian model, including a first iteration and a second iteration, wherein input model parameters for the second iteration are determined based at least in part on an evaluation of the objective function over a set of execution results obtained from the execution environment, wherein the set of execution results is obtained using settings indicated for at least the first and second tunable parameters in output model parameters of the first iteration;
• and
• in response to determining that an optimization goal associated with the execution environment has been attained according to the objective function, storing an indication of settings for at least the first and second tunable parameters which correspond to an attainment of the optimization goal.

A non-transitory computer-accessible storage medium storing program instructions that when executed on one or more processors:

• receiving, from a client, (a) a definition of an objective function that a non-linear transformation of one or more execution results produced by one or more software components in an execution environment to produce as output one or more objective function values to be optimized and (b) a respective plurality of permissible settings for at least a first tunable parameter and a second tunable parameter of a plurality of tunable parameters of the execution environment;
• generate, with respect to the execution environment, a Bayesian model employing a Gaussian process, wherein input to the Bayesian model includes the one or more objective function values and output of the Bayesian model includes settings for at least the first and second tunable parameters;
• implement a plurality of iterations of execution of the Bayesian model, including a first iteration and a second iteration, wherein input model parameters for the second iteration are determined based at least in part on an evaluation of the objective function over a set of execution results obtained from the execution environment, wherein the set of execution results is obtained using settings indicated for at least the first and second tunable parameters in output model parameters of the first iteration;
• and
• store an indication of settings indicated for at least the first and second tunable parameters in output model parameters of a selected iteration of the plurality of iterations of execution.