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A memetic grouping genetic algorithm for cost efficient VM placement in multi-cloud environment

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Abstract

The placement of a virtual machine in cloud computing generates a cost derived from consuming the energy of the allocated network elements. In this paper, we present an optimization model for effective virtual machine placement in the heterogeneous multi-cloud systems by considering peak demand time and geographical position of allocated resources, with target of minimizing the energy cost of allocated network elements. We also build a dynamic energy model for cloud physical machines and communication components. Then, we propose a correlation aware virtual machine placement algorithm, namely MGGAVP, with these issues in mind. The algorithm is based on the hybridization of the Grouping Genetic Algorithm and Hill-climbing and extended for the multi-cloud environment. The results of simulation reveal that the proposed algorithm can have significantly better performance than the three comparison algorithms with the energy saving of 51.93% average performance promotion and energy cost of 70.41% average performance promotion.

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Authors and Affiliations

  1. Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Seyedeh Yasaman Rashida & Amir Masoud Rahmani

  2. Department of Computer Engineering and Information Technology, Amirkabir University of Technology, Tehran, Iran

    Masoud Sabaei & Mohammad Mehdi Ebadzadeh

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  1. Seyedeh Yasaman Rashida
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  2. Masoud Sabaei
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  3. Mohammad Mehdi Ebadzadeh
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  4. Amir Masoud Rahmani
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Correspondence to Masoud Sabaei.

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Rashida, S.Y., Sabaei, M., Ebadzadeh, M.M. et al. A memetic grouping genetic algorithm for cost efficient VM placement in multi-cloud environment. Cluster Comput 23, 797–836 (2020). https://doi.org/10.1007/s10586-019-02956-8

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  • DOI: https://doi.org/10.1007/s10586-019-02956-8

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