Enhancing Smart Re-run of Kepler Scientific Workflows Based on Near Optimum Provenance Caching in Cloud

Both improving the execution efficiency and reducing the execution cost are essential for scientific workflows in cloud environments. As many scientific workflow tasks become more data-intensive and computation-intensive, storing their outputs in cloud for reuse is a feasible way to achieve such objectives. Because data storage in cloud would increase the storage cost, though it might mean less computation time due to data reuse, it is important to determine the proportion of the output data sets of workflow tasks that should be stored in cloud. The paper explores caching provenance in cloud to enhance the smart re-run of Kepler workflows based on a near optimum data caching policy. Ant Colony System optimization is introduced to determine the near optimum data sets to be cached in cloud in order to improve the execution efficiency of future workflow re-run without increasing the total cost of workflow execution in cloud. Simulation and analysis show that the proposed approach is efficient.