  The recent boom of big data, coupled with the challenges of its processing
and storage gave rise to the development of distributed data processing and
storage paradigms like MapReduce, Spark, and NoSQL databases. With the advent
of cloud computing, processing and storing such massive datasets on clusters of
machines is now feasible with ease. However, there are limited tools and
approaches, which users can rely on to gauge and comprehend the performance of
their big data applications deployed locally on clusters, or in the cloud.
Researchers have started exploring this area by providing benchmarking suites
suitable for big data applications. However, many of these tools are
fragmented, complex to deploy and manage, and do not provide transparency with
respect to the monetary cost of benchmarking an application.
  In this paper, we present Plug And Play Bench, an infrastructure aware
abstraction built to integrate and simplify the deployment of big data
benchmarking tools on clusters of machines. PAPB automates the tedious process
of installing, configuring and executing common big data benchmark workloads by
containerising the tools and settings based on the underlying cluster
deployment framework. Our proof of concept implementation utilises HiBench as
the benchmark suite, HDP as the cluster deployment framework and Azure as the
cloud platform. The paper further illustrates the inclusion of cost metrics
based on the underlying Microsoft Azure cloud platform.
