Description

Cloud Computing, a distributed computing paradigm, enables delivery of IT resources over the Internet and follows the pay-as-you-go billing model. Workflow scheduling is one of the most challenging problems in Cloud computing. Although, workflow scheduling on distributed systems like Grids and Clusters have been extensively studied, however, these solutions are not viable for a Cloud environment. It is because, a Cloud environment differs from other distributed environment in two major ways: on-demand resource provisioning and pay-as-you-go pricing model. Thus, to achieve the true benefits of workflow orchestration onto Cloud resources novel approaches that can capitalize the advantages and address the challenges specific to a Cloud environment needs to be developed. This work proposes a dynamic cost-effective deadline-constrained heuristic algorithm for scheduling a scientific workflow in a public Cloud. The proposed technique aims to exploit the advantages offered by Cloud computing while taking into account the virtual machine performance variability and instance acquisition delay to identify a just-in-time schedule of a deadline constrained scientific workflow at lesser costs. Performance evaluation on some well-known scientific workflows exhibit that the proposed algorithm delivers better performance in comparison to the current state-of-the-art heuristics