  Distributed data storage services tailored to specific applications have
grown popular in the high-performance computing (HPC) community as a way to
address I/O and storage challenges. These services offer a variety of specific
interfaces, semantics, and data representations. They also expose many tuning
parameters, making it difficult for their users to find the best configuration
for a given workload and platform.
  To address this issue, we develop a novel variational-autoencoder-guided
asynchronous Bayesian optimization method to tune HPC storage service
parameters. Our approach uses transfer learning to leverage prior tuning
results and use a dynamically updated surrogate model to explore the large
parameter search space in a systematic way.
  We implement our approach within the DeepHyper open-source framework, and
apply it to the autotuning of a high-energy physics workflow on Argonne's Theta
supercomputer. We show that our transfer-learning approach enables a more than
$40\times$ search speedup over random search, compared with a $2.5\times$ to
$10\times$ speedup when not using transfer learning. Additionally, we show that
our approach is on par with state-of-the-art autotuning frameworks in speed and
outperforms them in resource utilization and parallelization capabilities.
