Task Deadline-Aware Energy-Efficient Scheduling Model for a Virtualized Cloud

Data centers in cloud environment consume high amount of energy which not only raises the electricity bills of the data center hosting organizations but also has the strong environmental footprints. Therefore, energy efficiency of the data centers has become an important research issue. Many energy efficiency approaches have been proposed in the literature for cloud. Efficient resource scheduling is one of the important approaches to achieve energy efficiency in cloud. In this paper, a task deadline-aware energy-efficient scheduling model for virtualized cloud is presented. Independent and dynamically arriving deadline-aware tasks are scheduled by virtualizing the physical hosts in the data center. The proposed scheduling model at the first instance achieves the energy efficiency by executing maximum workload in the operational state of the host and at the second instance by maximum energy saving in the idle state of the host. In the operational state of the host, maximum workload is executed by exploiting the task slack time in a new context, and in the idle state of the host, maximum energy is saved by deploying core-level granularity of dynamic voltage and frequency scaling. The presented scheduling model is evaluated on the synthetic and real-world workload. Results clearly indicate that the presented scheduling model outperforms the existing scheduling model on the account of performance parameters of guarantee ratio, total energy consumption, energy consumption per task and resource utilization.