  Big data analytics frameworks (BDAFs) have been widely used for data
processing applications. These frameworks provide a large number of
configuration parameters to users, which leads to a tuning issue that
overwhelms users. To address this issue, many automatic tuning approaches have
been proposed. However, it remains a critical challenge to generate enough
samples in a high-dimensional parameter space within a time constraint. In this
paper, we present AutoTune--an automatic parameter tuning system that aims to
optimize application execution time on BDAFs. AutoTune first constructs a
smaller-scale testbed from the production system so that it can generate more
samples, and thus train a better prediction model, under a given time
constraint. Furthermore, the AutoTune algorithm produces a set of samples that
can provide a wide coverage over the high-dimensional parameter space, and
searches for more promising configurations using the trained prediction model.
AutoTune is implemented and evaluated using the Spark framework and HiBench
benchmark deployed on a public cloud. Extensive experimental results illustrate
that AutoTune improves on default configurations by 63.70% on average, and on
the five state-of-the-art tuning algorithms by 6%-23%.
