An Energy-aware Real-time Task Scheduling Approach in a Cloud Computing Environment

The interest in cloud computing has grown considerably over the recent years, primarily due to the scalable virtualized resources. Thus cloud computing has contributed to the advancement of the real-time applications such as the signal processing, environment surveillance, and weather forecast, where time and energy considerations are critical in order to perform the tasks. In the real-time applications, missing the deadlines for the tasks will cause catastrophic consequences Thus real-time task scheduling in a cloud computing environment is an important and essential issue. Furthermore, energy-saving in the cloud data center, regarding the benefits such as the reduction in the system operating costs and environmental protection is an important concern that has been considered during the recent years, and is reducible with an appropriate task scheduling. In this paper, we present an energy-aware real-time task (EaRT) scheduling approach for the real-time applications. We employ the virtualization and consolidation techniques subject to minimizing the energy consumptions, improve resource utilization, and meeting the deadlines of the tasks. In the consolidation technique, the scale-up and scale-down of the virtualized resources could improve the performance of task execution. The proposed approach comprises four algorithms, namely energy-aware task scheduling in cloud computing (ETC), vertical VM scale-up (V2S), horizontal VM scale-up (HVS), and physical machine scale-down (PSD). We present the formal model of the proposed approach using timed automata in order to prove precisely the schedulability feature and correctness of EaRTs. We will show that our proposed approach is more efficient in terms of the deadline hit ratio, resource utilization, and energy consumption compared to the other energy-aware real-time tasks scheduling algorithms.