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Energy-aware virtual machine allocation and selection in cloud data centers

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Abstract

Data centers evolve constantly in size, complexity, and power consumption. Energy management in cloud data centers is a critical and challenging research issue. It becomes necessary to minimize the operational costs as well as environmental impact and to guarantee the service-level agreements for the services provided by the data centers. We propose a modified discrete particle swarm optimization based on the characteristic particle swarm optimization for the initial placement of virtual machines and a novel virtual machine selection algorithm for optimizing the current allocation based on memory utilization, bandwidth utilization, and size of the virtual machine. By means of simulations, we observe that the proposed method not only saves the energy significantly than the other approaches, but also minimizes the violations of service-level agreements.

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Acknowledgements

This research received funding from the Netherlands Organization for Scientific Research (NWO) in the framework of the Indo Dutch Science Industry Collaboration programme in relation to project NextGenSmart DC (629.002.102). We thank Prof. Marco Aiello, University of Groningen, Netherlands for his useful insights and comments. We thank reviewers for their valuable and useful suggestions for the improvement of this article.

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Authors and Affiliations

  1. Institute for Development and Research in Banking Technology (IDRBT), Hyderabad, India

    V. Dinesh Reddy & G. R. Gangadharan

  2. School of Computer and Information Sciences, University of Hyderabad, Hyderabad, India

    V. Dinesh Reddy

  3. Cognizant Technology Solutions, Chennai, India

    G. Subrahmanya V. R. K. Rao

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  1. V. Dinesh Reddy
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  2. G. R. Gangadharan
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  3. G. Subrahmanya V. R. K. Rao
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Correspondence to G. R. Gangadharan.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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V. Dinesh Reddy, G. R. Gangadharan and G. Subrahmanya V. R. K. Rao declares that they have no conflict of interest.

Additional information

Communicated by V. Loia.

Appendix: Coordination of the particles

Appendix: Coordination of the particles

We simulated a data center comprising 100 heterogeneous physical machines and 300 virtual machines in the said experimental environment with the following initial parameters:

  • Population size \(=\) 40,

  • Inertia weight coefficients: \(k_1 = 3\) and \(k_2 = 2\).

These values are chosen after several experiments and the way these weights coordinate the searching process after each iteration is presented here. We presented the change in the fitness value of each particle, starting from the first iteration to termination with an interval of 20 in Fig. 6.

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Dinesh Reddy, V., Gangadharan, G.R. & Rao, G.S.V.R.K. Energy-aware virtual machine allocation and selection in cloud data centers. Soft Comput 23, 1917–1932 (2019). https://doi.org/10.1007/s00500-017-2905-z

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