A Probabilistic Batch Oriented Proactive Workflow Management

Workflow management is a widely studied research subject due to its criticality for the efficient execution of various processing activities towards concluding innovative applications. The ultimate goal is to eliminate the required time for delivering the final outcome considering the dependencies between workflow’s tasks. In this paper, we enhance the decision making of a scheduler with a batch oriented approach to deal with multiple workflows. A probabilistic data oriented approach combined with an infrastructure oriented scheme is provided to pay attention on dynamic environments where the underlying data are continuously updated trying to minimize the network overhead for migrating data. Workflows are mapped to the available datasets according to their data requirements, then, we combine the outcome with an optimization model upon the time and cost requirements of every placement. The performance of our model is revealed by a high number of experiments depicting the advantages in the network overhead.