  Choosing a good resource configuration for big data analytics applications
can be challenging, especially in cloud environments. Automated approaches are
desirable as poor decisions can reduce performance and raise costs. The
majority of existing automated approaches either build performance models from
previous workload executions or conduct iterative resource configuration
profiling until a near-optimal solution has been found. In doing so, they only
obtain an implicit understanding of the underlying infrastructure, which is
difficult to transfer to alternative infrastructures and, thus, profiling and
modeling insights are not sustained beyond very specific situations.
  We present Perona, a novel approach to robust infrastructure fingerprinting
for usage in the context of big data analytics. Perona employs common sets and
configurations of benchmarking tools for target resources, so that resulting
benchmark metrics are directly comparable and ranking is enabled. Insignificant
benchmark metrics are discarded by learning a low-dimensional representation of
the input metric vector, and previous benchmark executions are taken into
consideration for context-awareness as well, allowing to detect resource
degradation. We evaluate our approach both on data gathered from our own
experiments as well as within related works for resource configuration
optimization, demonstrating that Perona captures the characteristics from
benchmark runs in a compact manner and produces representations that can be
used directly.
