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VM Deployment Methods for DaaS Model in Clouds

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Advances in Internet, Data & Web Technologies (EIDWT 2018)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 17))

Abstract

Big Data has become an enabling technology for many of the today’s innovations. Given the exponential rate at which the data is produced there is a clear necessity for scalable solutions to control the overwhelming flow of new streams of information and extract information out of DaaS Clouds. In this paper we review and analyze some VM deployment methods and their suitability for Data as a Service (DaaS) model in Clouds. Then we approach some novel aspects of VM deployment, including VM migration.

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Author information

Authors and Affiliations

  1. Istituto Superiore Mario Boella, Turin, Italy

    Klodiana Goga & Olivier Terzo

  2. Universitat Politècnica de Catalunya, Barcelona, Spain

    Fatos Xhafa

Authors
  1. Klodiana Goga
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  2. Fatos Xhafa
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  3. Olivier Terzo
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Corresponding author

Correspondence to Klodiana Goga .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Fukuoka-shi, Japan

    Leonard Barolli

  2. Technical University of Catalonia, Barcelona, Spain

    Fatos Xhafa

  3. Department of Computer Science, COMSATS Institute of Information Technology, Islamabad, Pakistan

    Nadeem Javaid

  4. Polytechnic University of Tirana, Tirana, Albania

    Evjola Spaho

  5. Polytechnic University of Tirana, Tirana, Albania

    Vladi Kolici

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Goga, K., Xhafa, F., Terzo, O. (2018). VM Deployment Methods for DaaS Model in Clouds. In: Barolli, L., Xhafa, F., Javaid, N., Spaho, E., Kolici, V. (eds) Advances in Internet, Data & Web Technologies. EIDWT 2018. Lecture Notes on Data Engineering and Communications Technologies, vol 17. Springer, Cham. https://doi.org/10.1007/978-3-319-75928-9_33

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  • DOI: https://doi.org/10.1007/978-3-319-75928-9_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-75927-2

  • Online ISBN: 978-3-319-75928-9

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