Cost-effective deadline-aware stochastic scheduling strategy for workflow applications on virtual machines in cloud computing

This paper addresses the problems in scheduling the workflow tasks on cloud computing systems such as minimizing the total price for execution (TPE) and total execution time (TET) of the workflow while meeting the deadline constraints in a stochastic environment. Scheduling such precedence-constrained stochastic tasks on the cloud with virtual machines of different computing capabilities is a difficult problem. However, instead of TPE and TET, the virtual machine's acquisition delay is one of the primary cloud's characteristics. The current paper first formulates the problem as a stochastic scheduling model on cloud. Then, a stochastic cost-effective deadline-aware (S-CEDA) resource scheduler is developed. S-CEDA incorporates the expected value and variance of the task's processing time and inter-task communication time into the workflow scheduling. The experimental results show that S-CEDA outperforms the existing state-of-the-art algorithms such as stochastic heterogeneous earliest finish time (SHEFT) and cost-effective deadline-aware (CEDA) scheduling algorithms in terms of the TPE and TET of the workflow.