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Energy Aware Virtual Machine Scheduling in Data Centers

Author

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  • Yeliang Qiu

    (Key Laboratory of Complex Systems Modeling and Simulation, Ministry of Education, Hangzhou 310018, China
    School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China)

  • Congfeng Jiang

    (Key Laboratory of Complex Systems Modeling and Simulation, Ministry of Education, Hangzhou 310018, China
    School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China)

  • Yumei Wang

    (Key Laboratory of Complex Systems Modeling and Simulation, Ministry of Education, Hangzhou 310018, China
    School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China)

  • Dongyang Ou

    (Key Laboratory of Complex Systems Modeling and Simulation, Ministry of Education, Hangzhou 310018, China
    School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China)

  • Youhuizi Li

    (Key Laboratory of Complex Systems Modeling and Simulation, Ministry of Education, Hangzhou 310018, China
    School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China)

  • Jian Wan

    (Key Laboratory of Complex Systems Modeling and Simulation, Ministry of Education, Hangzhou 310018, China
    School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China)

Abstract

Power consumption is a primary concern in modern servers and data centers. Due to varying in workload types and intensities, different servers may have a different energy efficiency (EE) and energy proportionality (EP) even while having the same hardware configuration (i.e., central processing unit (CPU) generation and memory installation). For example, CPU frequency scaling and memory modules voltage scaling can significantly affect the server’s energy efficiency. In conventional virtualized data centers, the virtual machine (VM) scheduler packs VMs to servers until they saturate, without considering their energy efficiency and EP differences. In this paper we propose EASE, the Energy efficiency and proportionality Aware VM SchEduling framework containing data collection and scheduling algorithms. In the EASE framework, each server’s energy efficiency and EP characteristics are first identified by executing customized computing intensive, memory intensive, and hybrid benchmarks. Servers will be labelled and categorized with their affinity for different incoming requests according to their EP and EE characteristics. Then for each VM, EASE will undergo workload characterization procedure by tracing and monitoring their resource usage including CPU, memory, disk, and network and determine whether it is computing intensive, memory intensive, or a hybrid workload. Finally, EASE schedules VMs to servers by matching the VM’s workload type and the server’s EP and EE preference. The rationale of EASE is to schedule VMs to servers to keep them working around their peak energy efficiency point, i.e., the near optimal working range. When workload fluctuates, EASE re-schedules or migrates VMs to other servers to make sure that all the servers are running as near their optimal working range as they possibly can. The experimental results on real clusters show that EASE can save servers’ power consumption as much as 37.07%–49.98% in both homogeneous and heterogeneous clusters, while the average completion time of the computing intensive VMs increases only 0.31%–8.49%. In the heterogeneous nodes, the power consumption of the computing intensive VMs can be reduced by 44.22%. The job completion time can be saved by 53.80%.

Suggested Citation

  • Yeliang Qiu & Congfeng Jiang & Yumei Wang & Dongyang Ou & Youhuizi Li & Jian Wan, 2019. "Energy Aware Virtual Machine Scheduling in Data Centers," Energies, MDPI, vol. 12(4), pages 1-21, February.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:4:p:646-:d:206683
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    References listed on IDEAS

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    1. Yan Bai & Lijun Gu & Xiao Qi, 2018. "Comparative Study of Energy Performance between Chip and Inlet Temperature-Aware Workload Allocation in Air-Cooled Data Center," Energies, MDPI, vol. 11(3), pages 1-23, March.
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    4. Luca Chiaraviglio & Antonio Cianfrani & Marco Listanti & William Liu & Marco Polverini, 2016. "Lifetime-Aware Cloud Data Centers: Models and Performance Evaluation," Energies, MDPI, vol. 9(6), pages 1-17, June.
    5. Emelie Wibron & Anna-Lena Ljung & T. Staffan Lundström, 2018. "Computational Fluid Dynamics Modeling and Validating Experiments of Airflow in a Data Center," Energies, MDPI, vol. 11(3), pages 1-15, March.
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    Cited by:

    1. Zhiling Guo & Jin Li & Ram Ramesh, 2023. "Green Data Analytics of Supercomputing from Massive Sensor Networks: Does Workload Distribution Matter?," Information Systems Research, INFORMS, vol. 34(4), pages 1664-1685, December.
    2. Kaiqiang Zhang & Dongyang Ou & Congfeng Jiang & Yeliang Qiu & Longchuan Yan, 2021. "Power and Performance Evaluation of Memory-Intensive Applications," Energies, MDPI, vol. 14(14), pages 1-20, July.

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