Abstract
Cloud computing has a powerful ability to handle a large number of tasks. Correspondingly, it also consumes a lot of energy. Reducing the energy consumption of cloud service platforms while ensuring the quality of service has become a crucial issue. In this paper, we propose a heuristic energy-saving scheduling algorithm named Real-time Multi-workflow Energy-efficient Scheduling (RMES) with the aim to minimize the total energy consumption in container cloud. RMES executes tasks as parallel as possible to enhance the resource utilization of the running machines in cluster, therefore reducing the time of the global process, saving energy as a result. RMES takes advantage of the affinity between containers and machines to meet the resource quantity and performance requirements of containers during scheduling. In order to follow the change of the system state overtime, we introduce the re-scheduling mechanism, which can automatically adjust the scheduling decisions of the tasks that have not yet been executed in the scheduling scheme. The experimental results show that RMES has obvious advantages over other scheduling algorithms in terms of energy consumption and success ratio.
Zaixing Sun
PhD Candidate
I am currently pursuing a PhD degree in computer science and technology with the School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China. I am also a visiting student with the Evolutionary Computation Research Group, Centre for Data Science and Artificial Intelligence & School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand. My research interests include cloud computing, intelligent optimisation and scheduling.