Energy-efficient real-time multi-workflow scheduling in container-based cloud

Abstract

Cloud computing has a powerful capability to handle a large number of tasks. However, this capability comes with significant energy requirements. It is critical to overcome the challenge of minimizing energy consumption within cloud service platforms without compromising service quality. In this paper, we propose a heuristic energy-saving scheduling algorithm, called Real-time Multi-workflow Energy-efficient Scheduling (RMES), which aims to minimize the total energy consumption in a container-based cloud. RMES schedules tasks in the most parallelized way to improve the resource utilization of the running machines in the cluster, thus reducing the time of the global process and saving energy. This paper also considers the affinity constraints between containers and machines, and RMES has the ability to satisfy the resource quantity and performance requirements of containers during the scheduling process. We introduce a re-scheduling mechanism that automatically adjusts the scheduling decisions of remaining tasks to account for the dynamic system states over time. The results show that RMES outperforms other scheduling algorithms in energy consumption and success rate. In the higher arrival rate scenario, the proposed algorithm saves energy consumption by more than 19.42%. The RMES approach can enhance the reliability and efficiency of scheduling systems.