A Responsive Knapsack-Based Algorithm for Resource Provisioning and Scheduling of Scientific Workflows in Clouds

Scientific workflows are used to process vast amounts of data and to conduct large-scale experiments and simulations. They are time consuming and resource intensive applications that benefit from running in distributed platforms. In particular, scientific workflows can greatly leverage the ease-of-access, affordability, and scalability offered by cloud computing. To achieve this, innovative and efficient ways of orchestrating the workflow tasks and managing the compute resources in a cost-conscious manner need to be developed. We propose an adaptive, resource provisioning and scheduling algorithm for scientific workflows deployed in Infrastructure as a Service clouds. Our algorithm was designed to address challenges specific to clouds such as the pay-as-you-go model, the performance variation of resources and the on-demand access to unlimited, heterogeneous virtual machines. It is capable of responding to the dynamics of the cloud infrastructure and is successful in generating efficient solutions that meet a user-defined deadline and minimise the overall cost of the used infrastructure. Our simulation experiments demonstrate that it performs better than other state-of-the-art algorithms.