Multi-priority scheduling algorithm for scientific workflows in cloud

The public cloud environment has emerged as a promising platform for executing scientific workflows. These executions involve leasing virtual machines
(VMs) from public services for the duration of the workflow. The structure of
the workflows significantly impacts the performance of any proposed scheduling
approach. A task within a workflow cannot begin its execution before receiving
all the required data from its preceding tasks. In this paper, we introduce a
multi-priority scheduling approach for executing workflow tasks in the cloud.
The key component of the proposed approach is a mechanism that logically orders and groups workflow tasks based on their data dependencies and locality.
Using the proposed approach, the number of available VMs influences the number of groups (partitions) obtained. Based on the locality of each group’s tasks,
the priority of each group is determined to reduce the overall execution delay
and improve VM utilization. As the results demonstrate, the proposed approach
achieves a significant reduction in both execution costs and time in most scenarios.