Energy and Reliability-Aware Task Scheduling for Cost Optimization of DVFS-Enabled Cloud Workflows

Due to the increasing complexity, the execution of workflow applications on cloud typically involves a large number of virtual machines (VMs), which makes the cost as well as energy consumption a great concern. To alleviate this issue, more and more cloud service providers introduce new pricing policies considering Dynamic Voltage and Frequency Scaling (DVFS), where users are charged on the basis of allocated CPU frequencies together with various combinations of VM configurations and prices. However, the customizable CPU frequencies make resource provisioning and scheduling harder to achieve a cost-optimal solution. The things become even worse, since lowering CPU voltages of VMs will increase their chance of suffering soft errors, which results in a high rate of completion time failures of workflow applications. To address the above problem, this paper proposes a novel task scheduling method for the purpose of cost optimization based on the genetic algorithm. By introducing new genetic operators and frequency scaling scheme for DVFS-enabled cloud workflows, our approach can quickly figure out cost-optimal resource provisioning and task scheduling solutions by allocating tasks to appropriate VMs with specific operating frequencies under energy, reliability, makespan and memory constraints. Extensive experiments on various well-known scientific workflow benchmarks validate the effectiveness of the proposed method. Comparing with state-of-the-art methods, our approach can significantly reduce the overall cost and energy consumption without violating the given constraints.