Task scheduling strategy based on data replication in scientific Cloud workflows

The invention of Cloud computing as a new model of service provisioning in distributed systems encourages researchers to investigate its benefits and drawbacks in executing applications. In recent years, Cloud computing is fast evolving as the target platform for such applications among researchers. Furthermore, new pricing models have been pioneered by Cloud providers that allow users to provision resources and to use them in an efficient manner with significant cost reductions. Approaches for scheduling and data placement is often highly correlated, which take into account a few factors at the same time, and what are the most often adapted to applications data medium and therefore doesn't go to scale. In this work, we propose an optimization approach that takes into account an effective data placement and scheduling of tasks grouped based on data replication in scientific Cloud environments. This proposed approach improve data placement and minimize response time due to scheduling tasks to data centers that contain the majority of the required data.