Budget-Constrained Resource Provisioning for Scientific Applications in Clouds

Public commercial clouds emerged as new and attractive resource provisioning option for scientific computing. This new alternative raises new challenges for users of such clouds, since optimizing the completion time of scientific applications might substantially increase the monetary cost of leasing cloud resources. In this paper, we first propose a set of basic rescheduling operations covering a broad set of scenarios for reducing the costs of running scientific workflows in clouds. Based on them, we design two heuristic scheduling algorithms. The first algorithm aims at reducing the cost of resource provisioning while still attaining the optimal make span. The second algorithm further reduces the costs to meet a budget constraint with a small increase in the make span. The experiments conducted using real-world and synthetic workflow applications demonstrate important benefits compared to related state-of-the-art approaches.