  Understanding and predicting the performance of big data applications running
in the cloud or on-premises could help minimise the overall cost of operations
and provide opportunities in efforts to identify performance bottlenecks. The
complexity of the low-level internals of big data frameworks and the ubiquity
of application and workload configuration parameters makes it challenging and
expensive to come up with comprehensive performance modelling solutions.
  In this paper, instead of focusing on a wide range of configurable
parameters, we studied the low-level internals of the MapReduce communication
pattern and used a minimal set of performance drivers to develop a set of phase
level parametric models for approximating the execution time of a given
application on a given cluster. Models can be used to infer the performance of
unseen applications and approximate their performance when an arbitrary dataset
is used as input. Our approach is validated by running empirical experiments in
two setups. On average the error rate in both setups is plus or minus 10% from
the measured values.
