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The Journal of Supercomputing

, Volume 73, Issue 9, pp 3977–3998 | Cite as

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

  • Meera Vasudevan
  • Yu-Chu Tian
  • Maolin Tang
  • Erhan Kozan
  • Weizhe Zhang
Article

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.

Keywords

Data center Application assignment Dynamic allocation Energy efficiency Genetic algorithm 

Notes

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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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.School of Electrical Engineering and Computer ScienceQueensland University of TechnologyBrisbaneAustralia
  2. 2.School of Mathematical SciencesQueensland University of TechnologyBrisbaneAustralia
  3. 3.School of Computer Science and TechnologyHarbin Institute and TechnologyHarbinPeople’s Republic of China

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