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A stochastic process-based server consolidation approach for dynamic workloads in cloud data centers

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Abstract

With the development of information technology, there is a need for computational works everywhere and every time. Thus, people should be able to carry out their heavy computations without having the burden of purchasing expensive hardware and software. Cloud computing is an attractive solution to such needs, but the high energy consumption of physical machines in a Cloud data center is a matter of great concern. Therefore, some of the low-loaded machines can be turned off or switched into low energy mode using server consolidation approaches. In this paper, a Stochastic Process-Based Dynamic Server Consolidation (SB-DSC) policy is developed to reduce the total cost of data centers while satisfying the required quality of service. A novel algorithm, which we call it Stochastic Process-Based BFD (SBBFD), is employed in SB-DSC policy to perform virtual machine placements over time. SBBFD overcomes most drawbacks of other algorithms proposed in the literature. The simulation results on real workload data show that SB-DSC leads to a noticeable reduction in total cost in terms of power consumption, SLA violations, number of mode switching and number of migrations.

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Notes

  1. The OpenStack Cloud platform. http://openstack.org/.

  2. OpenNebula Cloud manager. https://opennebula.org/.

  3. The Snooze Cloud manager. http://snooze.inria.fr/.

  4. http://monshizade.com/sb-dsc/.

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Authors and Affiliations

  1. Faculty of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran

    Hossein Monshizadeh Naeen, Esmaeil Zeinali & Abolfazl Toroghi Haghighat

Authors
  1. Hossein Monshizadeh Naeen
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  2. Esmaeil Zeinali
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  3. Abolfazl Toroghi Haghighat
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Correspondence to Esmaeil Zeinali.

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Monshizadeh Naeen, H., Zeinali, E. & Toroghi Haghighat, A. A stochastic process-based server consolidation approach for dynamic workloads in cloud data centers. J Supercomput 76, 1903–1930 (2020). https://doi.org/10.1007/s11227-018-2431-5

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