Scheduling Workflows With Limited Budget to Cloud Server and Serverless Resources

Serverless functions (SFs) and on-demand virtual machines (VMs) are common cloud resources for scientific workflow applications, which are widespread in many fields. SFs are paid by actual running time with higher unit costs and higher resource utilization than VMs which are paid by billing time units. Generally, each application is executed on a limited budget. In this article, we study the challenging cloud workflow scheduling problem with a limited budget to minimize makespan in a hybridization of SFs and on-demand VMs for which the BCWS (Budget Constrained Workflow Scheduling) algorithm is proposed. Methods are developed to determine the task execution order, rent cloud resources and map tasks to resources respectively. Together with initial schedule construction and schedule improvement policies, these procedures are repeatedly applied in BCWS. The proposed algorithm is evaluated by comparing it to existing algorithms for similar problems over a comprehensive set of workflow instances. Experimental results show that the proposed algorithm significantly reduces the makespan with a hybrid configuration of VMs and SFs compared to the server only or the serverless only configurations and outperforms the compared algorithms which are the best existing ones for similar problems.