Energy-Aware Job Management Approaches for Workflow in Cloud

The energy consumption of cloud servers has dramatically increased. In order to meet the growing demands of users and reduce the skyrocketing cost of electricity, it is critical to have performance guaranteed and cost-effective job schedulers for clouds. In recent years, there has been a growing body of research which focus on improving resource utilization to improve energy efficiency, system throughput and at the same time meet the Quality of Service (QoS) requirements specified in the Service Level Agreements (SLA). This paper propose a multiple procedure scheduling algorithm which aims to maximize the resource utilization for cloud resources for reduced energy consumption as well as guarantee the execution deadline for cloud jobs modeled as scientific workflows. Our simulation results demonstrate better performance compared with other similar algorithms.