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Energy efficiency of VM consolidation in IaaS clouds

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Abstract

The energy efficiency of cloud computing has recently attracted a great deal of attention. As a result of raised expectations, cloud providers such as Amazon and Microsoft have started to deploy a new IaaS service, a MapReduce-style virtual cluster, to process data-intensive workloads. Considering that the IaaS provider supports multiple pricing options, we study batch-oriented consolidation and online placement for reserved virtual machines (VMs) and on-demand VMs, respectively. For batch cases, we propose a DVFS-based heuristic TRP-FS to consolidate virtual clusters on physical servers to save energy while guarantee job SLAs. We prove the most efficient frequency that minimizes the energy consumption, and the upper bound of energy saving through DVFS techniques. More interestingly, this frequency only depends on the type of processor. FS can also be used in combination with other consolidation algorithms. For online cases, a time-balancing heuristic OTB is designed for on-demand placement, which can reduce the mode switching by means of balancing server duration and utilization. The experimental results both in simulation and using the Hadoop testbed show that our approach achieves greater energy savings than existing algorithms.

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Authors and Affiliations

  1. School of Information Science and Technology, Southwest Jiaotong University, Chengdu, 610031, China

    Fei Teng, Tianrui Li & Danting Deng

  2. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210093, China

    Fei Teng

  3. Sino-French Engineering School, Beihang University, Beijing, 100191, China

    Lei Yu

  4. Ecole Centrale Paris, Grande Voie des Vignes, 92295, Châtenay-Malabry, France

    Frédéric Magoulès

Authors
  1. Fei Teng
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  2. Lei Yu
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  3. Tianrui Li
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  4. Danting Deng
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  5. Frédéric Magoulès
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Corresponding author

Correspondence to Lei Yu.

Additional information

This work was partially supported by the National Natural Science Foundation of China (No. 61202043), the Soft Science Foundation of Sichuan Province (No. 2016ZR0034), the Open Research Fund of Key Laboratory of Network Intelligent Information Processing (No. SZJJ2014-049) and the State Key Laboratory of Software Development Environment of P. R. China under Grant (No. SKLSDE-2016ZX-25).

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Teng, F., Yu, L., Li, T. et al. Energy efficiency of VM consolidation in IaaS clouds. J Supercomput 73, 782–809 (2017). https://doi.org/10.1007/s11227-016-1797-5

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  • DOI: https://doi.org/10.1007/s11227-016-1797-5

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