A task backfill based scientific workflow scheduling strategy on cloud platform

The characteristics of cloud computing such as on-demand provisioning of virtual machines in a pay-as-you-go manner have attracted more and more scientific workflows deploying on cloud platforms. Since there are many types of virtual machines which are charged by time intervals, the difficulties of resource provisioning hinders efficient execution of scientific workflows on cloud platforms. To address the challenge, a novel task backfill based scientific workflow scheduling strategy is proposed in this paper. The strategy will use task backfill algorithm to aggregate multiple tasks on a virtual machine instance with suitable performance and fill idle time slot of virtual machines with single tasks, improving resource utilization without affecting the overall performance. Experimental results demonstrate that in comparison with widely used HEFT and IC-PCPD2 strategy, the proposed strategy can effectively reduce the execution cost of the scientific workflows and improve resource utilization while satisfying the deadline constraint.