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Profile-based dynamic application assignment with a repairing genetic algorithm for greener data centers

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Abstract

Data centers have become essential to modern society by catering to increasing number of Internet users and technologies. This results in significant challenges in terms of escalating energy consumption. Research on green initiatives that reduce energy consumption while maintaining performance levels is exigent for data centers. However, energy efficiency and resource utilization are conflicting in general. Thus, it is imperative to develop an application assignment strategy that maintains a trade-off between energy and quality of service. To address this problem, a profile-based dynamic energy management framework is presented in this paper for dynamic application assignment to virtual machines (VMs). It estimates application finishing times and addresses real-time issues in application resource provisioning. The framework implements a dynamic assignment strategy by a repairing genetic algorithm (RGA), which employs realistic profiles of applications, virtual machines and physical servers. The RGA is integrated into a three-layer energy management system incorporating VM placement to derive actual energy savings. Experiments are conducted to demonstrate the effectiveness of the dynamic approach to application management. The dynamic approach produces up to 48% better energy savings than existing application assignment approaches under investigated scenarios. It also performs better than the static application management approach with 10% higher resource utilization efficiency and lower degree of imbalance.

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Acknowledgements

This work is supported in part by the Australian Research Council (ARC) under the Discovery Projects Scheme Grant No. DP170103305 to Y-C. Tian.

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Authors and Affiliations

  1. School of Electrical Engineering and Computer Science, Queensland University of Technology, GPO Box 2434, Brisbane, QLD, 4001, Australia

    Meera Vasudevan, Yu-Chu Tian & Maolin Tang

  2. School of Mathematical Sciences, Queensland University of Technology, GPO Box 2434, Brisbane, QLD, 4001, Australia

    Erhan Kozan

  3. School of Computer Science and Technology, Harbin Institute and Technology, Harbin, 150001, Heilongjiang, People’s Republic of China

    Weizhe Zhang

Authors
  1. Meera Vasudevan
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  2. Yu-Chu Tian
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  3. Maolin Tang
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  4. Erhan Kozan
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  5. Weizhe Zhang
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Correspondence to Yu-Chu Tian.

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Vasudevan, M., Tian, YC., Tang, M. et al. Profile-based dynamic application assignment with a repairing genetic algorithm for greener data centers. J Supercomput 73, 3977–3998 (2017). https://doi.org/10.1007/s11227-017-1995-9

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  • DOI: https://doi.org/10.1007/s11227-017-1995-9

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