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Virtual Machine Scaling in Autonomic Cloud Resource Management

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Autonomic Computing in Cloud Resource Management in Industry 4.0

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

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Abstract

Virtualization is a technique that uses the hypervisor to create an abstract layer over the underlying hardware and allows the resources of this host such as memory, processor to be multiplexed among the guest operating system. It allows full utilization of the underlying hardware of the physical computer system. The guest operating systems that run over the host are called virtual machines. Sometimes, the load on these virtual machines may increase or decrease suddenly which requires to manage the resources multiplexed to the virtual machine accordingly. This feature is called virtual machine scaling. VM scaling is required for the proper management of the resources or underlying hardware and also for the efficient processing of the tasks in the virtual machines. The main factors that contributed to the need of virtual machine scaling included performance, cost, increased capacity, energy, and availability. Virtual machine scaling technique can be implemented through two methods: horizontal scaling and vertical scaling. The modes or policies which are generally used for VM scaling are categorized as: reactive mode, proactive mode, and combination of both reactive and proactive mode called the hybrid mode. The chapter covers the details of virtual machine scaling, its relation with elasticity, different methods and modes for implementing VM scaling, and also the research challenges faced in this sub-domain of cloud computing. The chapter will cover the main state of the art algorithms under methods and modes for virtual machine scaling.

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Author information

Authors and Affiliations

  1. Department of Virtualization, University of Petroleum and Energy Studies, Dehradun, India

    Avita Katal, Vitesh Sethi & Saksham Lamba

Authors
  1. Avita Katal
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  2. Vitesh Sethi
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  3. Saksham Lamba
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Editor information

Editors and Affiliations

  1. Department of Informatics, School of Computer Science University of Petroleum and Energy Studies (UPES), Dehradun, Uttarakhand, India

    Tanupriya Choudhury

  2. Department of Informatics, School of Computer Science University of Petroleum and Energy Studies (UPES), Dehradun, Uttarakhand, India

    Bhupesh Kumar Dewangan

  3. Department of Informatics, School of Computer Science University of Petroleum and Energy Studies (UPES), Dehradun, Uttarakhand, India

    Ravi Tomar

  4. Computing and Software Engineering, Arba Minch University, Arba Minch, Ethiopia

    Bhupesh Kumar Singh

  5. Nanyang Technological University (NTU), Singapore, Singapore

    Teoh Teik Toe

  6. Duy Tan University, Da Nang, Vietnam

    Nguyen Gia Nhu

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Katal, A., Sethi, V., Lamba, S. (2021). Virtual Machine Scaling in Autonomic Cloud Resource Management. In: Choudhury, T., Dewangan, B.K., Tomar, R., Singh, B.K., Toe, T.T., Nhu, N.G. (eds) Autonomic Computing in Cloud Resource Management in Industry 4.0. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-71756-8_17

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  • DOI: https://doi.org/10.1007/978-3-030-71756-8_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-71755-1

  • Online ISBN: 978-3-030-71756-8

  • eBook Packages: EngineeringEngineering (R0)

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