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Cluster Computing

Erschienen in:

18.07.2019

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

verfasst von: Seyedeh Yasaman Rashida, Masoud Sabaei, Mohammad Mehdi Ebadzadeh, Amir Masoud Rahmani

Erschienen in: Cluster Computing | Ausgabe 2/2020

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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.

Vorheriger Artikel A survey: ICT enabled energy efficiency techniques for big data applications

Nächster Artikel Optimizing virtual machine placement in IaaS data centers: taxonomy, review and open issues

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Titel: A memetic grouping genetic algorithm for cost efficient VM placement in multi-cloud environment
verfasst von: Seyedeh Yasaman Rashida
Masoud Sabaei
Mohammad Mehdi Ebadzadeh
Amir Masoud Rahmani
Publikationsdatum: 18.07.2019
Verlag: Springer US
Erschienen in: Cluster Computing / Ausgabe 2/2020
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI: https://doi.org/10.1007/s10586-019-02956-8

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