Scheduling workflows in cloud environments is an important issue that many types of research have been conducted in this field. However, these approaches often focus on single workflow scheduling while the need for scheduling multiple workflows is growing. This study aims at presenting a cloud Broker for executing Deadline-constrained Periodic scientific Workflows (BDPW). BDPW acts as a Workflow as a Service (WaaS) broker and uses both reserved and on-demand resources in order to minimize the monetary cost of renting resources from a cloud provider. Furthermore, BDPW uses container technology by executing multiple containerized tasks on the same Virtual Machine (VM) to decrease the provisioning delay of VMs. The proposed broker uses a hybrid scheduling method, i.e., static planning and dynamic scheduling. The static planner uses Resource Leveling Problem (RLP) to provide a scheduling plan and also recognizes the number of reserved resources that should be leased from a provider. Then, the dynamic scheduler tries to assign tasks to the reserved resources based on the primary static plan and leases on-demand instances if necessary. Also, it may make changes to the primary plan due to uncertainties in the task runtimes. The experimental results in CloudSim show that BDPW outperforms baseline algorithms in terms of monetary cost.

Keywords