With the increased popularity of cloud computing, the number and scales of cloud data centers have kept growing at unprecedented speeds. In the meanwhile, the energy consumption by the data centers has kept commensurately increasing as well. Therefore, the focus of cloud resource management and scheduling has relatively shifted from mere performance to also energy efficiency. In this paper, we present a novel, Energy-Aware Task Scheduling framework that makes integrated exploitation of the two well-known energy saving techniques, DVFS and VM Reuse, on cloud task scheduling in a data center. We present our scheduling approach and framework via a specific algorithm, called EATS-FFD, that assumes FFD as its base scheduling policy. With minor modification, the presented framework can be made to work with a different base scheduling policy, resulting in a correspondingly different scheduling algorithm. Our approach achieves better energy-efficiency without sacrificing system QoS. The effectiveness of our approach is evaluated under various experimental scenarios using the Cloud Report tool running on the open source CloudSim platform.

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.