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Recent advancement in VM task allocation system for cloud computing: review from 2015 to2021.

Arif Ullah1, Nazri Mohd Nawi1, Soukaina Ouhame2

  • 1Soft Computing and Data Mining Centre (SMC), Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia (UTHM), Parit Raja, Malaysia.

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|September 28, 2021
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Summary

This review examines virtual machine (VM) efficiency improvements in cloud computing from 2015-2020. It highlights how optimizing VM parameters enhances overall cloud data center performance and efficiency.

Keywords:
Cloud computingData distributionLoad balancing approachVMVirtualization

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Area of Science:

  • Computer Science
  • Cloud Computing
  • Virtualization Technology

Background:

  • Cloud computing has become integral to modern life, especially post-COVID-19, with resources accessed on a pay-per-user basis.
  • Virtualized resources, particularly virtual machines (VMs), are fundamental to cloud data centers for resource distribution.
  • Cloud data centers face performance and efficiency challenges that necessitate continuous improvement strategies.

Purpose of the Study:

  • To review advancements in virtual machine (VM) efficiency within cloud computing environments between 2015 and 2020.
  • To discuss key parameters influencing VM performance, including makespan, quality of service, energy consumption, data accuracy, and network utilization.
  • To explore the role of machine learning algorithms and load balancing techniques in enhancing VM efficiency.

Main Methods:

  • Literature review of research published between 2015 and 2020 focusing on virtual machine improvements in cloud data centers.
  • Analysis of various approaches and parameters for enhancing virtual machine efficiency.
  • Examination of machine learning algorithms and load balancing strategies applied to virtual machines.

Main Results:

  • Improvements in virtual machine parameters like makespan, quality of service, energy efficiency, data accuracy, and network utilization directly boost cloud computing performance.
  • Various approaches have been identified and reviewed for enhancing virtual machine efficiency.
  • Machine learning algorithms and load balancing techniques show significant potential in optimizing VM performance.

Conclusions:

  • Optimizing virtual machine efficiency is crucial for improving overall cloud data center performance and resource management.
  • The review provides insights into the evolution of VM improvements and their impact on cloud computing.
  • Future research directions point towards advanced machine learning applications and sophisticated load balancing for virtual machines in cloud environments.