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2017 IEEE Symposium on Computers and Communications (ISCC) (2017)
Heraklion, Greece
July 3, 2017 to July 6, 2017
ISBN: 978-1-5386-1630-7
pp: 838-843
Youshi Wang , University of Chinese Academy of Sciences, Beijing, China
Fa Zhang , Key Laboratory of Intelligent Information Processing, ICT, CAS, Beijing, China
Rui Wang , Hong Kong University of Science and Technology, China
Yangguang Shi , Technion - Israel Institute of Technology, Haifa, Israel
Hua Guo , State Grid Jingzhou Electric Power Company, Jingzhou, Hubei, China
Zhiyong Liu , Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology (ICT), Chinese Academy of Sciences (CAS), China
The high energy consumption has become one bottleneck in the development of the data centers (DCs), where the main energy consumers are the cooling system and the servers. Therefore, the joint optimization for the energy efficiency of the cooling system and the servers is a crucial problem, while most of previous works on energy saving only studies one of these two components in an isolated manner. In this paper, we propose a real-time strategy, rTCS (real-time Task Classification and Scheduling strategy), to jointly optimize the energy efficiency of these two components in the scenario where the tasks arrive dynamically. Strategy rTCS first labels the tasks to classify them according to their run time and end time with a time complexity of O(1) and a bounded space complexity. Then, rTCS schedules the tasks in real time based on their labels and the energy consumption model of the DC. Simulation results show that rTCS can effectively improve the energy efficiency of DCs.
Cooling, Servers, Power demand, Energy consumption, Real-time systems, Atmospheric modeling, Classification algorithms

Y. Wang, F. Zhang, Rui Wang, Y. Shi, Hua Guo and Z. Liu, "Real-time Task Scheduling for joint energy efficiency optimization in data centers," 2017 IEEE Symposium on Computers and Communications (ISCC), Heraklion, Greece, 2017, pp. 838-843.
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