A Cooperative Two-Tier Energy-Aware Scheduling for Real-Time Tasks in Computing Clouds

Customers in a cloud would like to receive the results of their task as soon as possible while paying less. On the other hand, cloud providers aim to mitigate the operational cost of cloud environments. In other words, a limited budget makes providers create efficient cloud systems that utilize the computational powers of the clouds while minimizing their energy consumptions and environmental footprints. One of the prevalent techniques in mitigating the total energy consumptions of data-centers is through using consolidation of virtual machines (VMs). However, it incurs significant overheads on both computing resources and network infrastructure of a cloud. Furthermore, it causes tasks to be accomplished later or even it might lead to System Level Agreement (SLA) violations. To address the aforementioned challenges, we propose a cooperative two-tier task scheduling approach to benefit both cloud providers and their customers. It regulates the execution speeds of real-time tasks in a way that a host reaches the optimum level of utilization instead of migrating its tasks to other hosts. We also propose several predictive global task scheduling policies to map arrived tasks to feasible VMs. The simulation results show that the proposed task scheduling approach not only reduces the total energy consumption of a cloud by 41%, but also has profound impacts on turnaround times of real-time tasks by 85%.