Robust Scheduling of Scientific Workflows with Deadline and Budget Constraints in Clouds

Dynamic resource provisioning and the notion of seemingly unlimited resources are attracting scientific workflows rapidly into Cloud computing. Existing works on workflow scheduling in the context of Clouds are either on deadline or cost optimization, ignoring the necessity for robustness. Robust scheduling that handles performance variations of Cloud resources and failures in the environment is essential in the context of Clouds. In this paper, we present a robust scheduling algorithm with resource allocation policies that schedule workflow tasks on heterogeneous Cloud resources while trying to minimize the total elapsed time (makespan) and the cost. Our results show that the proposed resource allocation policies provide robust and fault-tolerant schedule while minimizing makespan. The results also show that with the increase in budget, our policies increase the robustness of the schedule.