Energy-Aware Scheduling Based on Marginal Cost and Task Classification in Heterogeneous Data Centers

Kaixuan Ji, Ce Chi, Fa Zhang, Antonio Fernández Anta, Penglei Song, Avinab Marahatta, Youshi Wang and Zhiyong Liu
Additional contact information
Kaixuan Ji: High Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100095, China
Ce Chi: High Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100095, China
Fa Zhang: High Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100095, China
Antonio Fernández Anta: IMDEA Networks Institute, Avda. del Mar Mediterraneo, 22, 28918 Leganes, Spain
Penglei Song: Information Engineering College, Capital Normal University, Beijing 100048, China
Avinab Marahatta: Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China
Youshi Wang: Meituan-Dianping Group, Beijing 100102, China
Zhiyong Liu: High Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100095, China

Energies, 2021, vol. 14, issue 9, 1-26

Abstract: The energy consumption problem has become a bottleneck hindering further development of data centers. However, the heterogeneity of servers, hybrid cooling modes, and extra energy caused by system state transitions increases the complexity of the energy optimization problem. To deal with such challenges, in this paper, an Energy Aware Task Scheduling strategy (EATS) utilizing marginal cost and task classification method is proposed that cooperatively improves the energy efficiency of servers and cooling systems. An energy consumption model for servers, cooling systems, and state transition is developed, and the energy optimization problem in data centers is formulated. The concept of marginal cost is introduced to guide the task scheduling process. The task classification method is incorporated with the idea of marginal cost to further improve resource utilization and reduce the total energy consumption of data centers. Experiments are conducted using real-world traces, and energy reduction results are compared. Results show that EATS achieves more energy-savings of servers, cooling systems, state transition in comparison to the other two techniques under a various number of servers, cooling modules and task arrival intensities. It is validated that EATS is effective at reducing total energy consumption and improving the resource utilization of data centers.

Keywords: data center; energy-aware; marginal cost; task scheduling; cooling system; task classification (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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