  This report is part of the DataflowOpt project on optimization of modern
dataflows and aims to introduce a data quality-aware cost model that covers the
following aspects in combination: (1) heterogeneity in compute nodes, (2)
geo-distribution, (3) massive parallelism, (4) complex DAGs and (5) streaming
applications. Such a cost model can be then leveraged to devise cost-based
optimization solutions that deal with task placement and operator
configuration.
