  Optimization of data placement in complex scientific workflows has become
very crucial since the large amounts of data generated by these workflows
significantly increases the turnaround time of the end-to-end application. It
is almost impossible to make an optimal scheduling for the end-to-end workflow
without considering the intermediate data movement. In order to reduce the
complexity of the workflow-scheduling problem, most of the existing work
constrains the problem space by some unrealistic assumptions, which result in
non-optimal scheduling in practice. In this study, we propose a genetic
data-aware algorithm for the end-to-end workflow scheduling problem. Distinct
from the past research, we develop a novel data-aware evaluation function for
each chromosome, a common augmenting crossover operator and a simple but
effective mutation operator. Our experiments on different workflow structures
show that the proposed GA based approach gives a scheduling close to the
optimal one.

Distributed, Parallel, and Cluster Computing