Cost-effective approaches for deadline-constrained workflow scheduling in clouds

Nowadays, heterogeneous cloud resources are charged by cloud providers according to the pay-as-you-go pricing model. To execute workflow applications in clouds under deadline constraints, cloud resources have to be utilized appropriately and judiciously, challenging traditional workflow scheduling algorithms, which are either inapplicable to the cloud environment or fail to fully exploit the features of scheduling problem for cost optimization. In this paper, we propose a heuristic algorithm CSDW and a meta-heuristic algorithm N-WOA to minimize the execution cost of the given workflow subject to the deadline constraint in clouds. CSDW first assigns the sub-deadline to each task based on the modified probabilistic upward rank, and then tasks are sorted and mapped to appropriate instances, finally instance-type upgrading and downgrading method is adopted to further accelerate workflow execution and reduce the total cost, respectively. N-WOA employs whale optimization algorithm for deadline-constrained cost optimization by refining the task ordering step in CSDW. By simulation experiments on scientific workflows with existing algorithms, the results demonstrate the capability of the proposed algorithms in meeting the deadlines and reducing the execution costs, CSDW is highly competitive and N-WOA achieves the best performance in all cases.