  Finding optimal configurations for Stream Processing Systems (SPS) is a
challenging problem due to the large number of parameters that can influence
their performance and the lack of analytical models to anticipate the effect of
a change. To tackle this issue, we consider tuning methods where an
experimenter is given a limited budget of experiments and needs to carefully
allocate this budget to find optimal configurations. We propose in this setting
Bayesian Optimization for Configuration Optimization (BO4CO), an auto-tuning
algorithm that leverages Gaussian Processes (GPs) to iteratively capture
posterior distributions of the configuration spaces and sequentially drive the
experimentation. Validation based on Apache Storm demonstrates that our
approach locates optimal configurations within a limited experimental budget,
with an improvement of SPS performance typically of at least an order of
magnitude compared to existing configuration algorithms.

Distributed, Parallel, and Cluster Computing