Application of virtual machine consolidation in cloud computing systems

Zolfaghari, Rahmat; Sahafi, Amir; Rahmani, Amir Masoud; Rezaei, Reza

doi:10.1016/j.suscom.2021.100524

Cited by 17 publications

(14 citation statements)

References 73 publications

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“…Meanwhile, as mentioned in the previous section, [22], [23] investigated evolutionary algorithms. We refer the interested reader to recent surveys [31], [37], [38] for a more exhaustive literature coverage.…”

Section: Optimization For Data Center Resource Allocationmentioning

confidence: 99%

Optimization of Data and Energy Migrations in Mini Data Centers for Carbon-Neutral Computing

Silva

Gamatié

Sassatelli

et al. 2023

IEEE Trans. Sustain. Comput.

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Due to large-scale applications and services, cloud computing infrastructures are experiencing an ever-increasing demand for computing resources. At the same time, the overall power consumption of data centers has been rising beyond 1% of worldwide electricity consumption. The usage of renewable energy in data centers contributes to decreasing their carbon footprint and overall electricity costs. Several green-energy-aware resource allocation approaches have been studied recently. None of them takes advantage of the joint migration of jobs and energy in green data centers to increase energy efficiency. This paper presents an optimization approach for energy-efficient resource allocation in mini data centers. The observed momentum around edge computing makes the design of geographically distributed mini data centers highly desirable. Our solution exploits both virtual machines (VMs) and energy migrations between green compute nodes in mini data centers. These nodes have energy harvesting, storage, and transport capabilities. They enable the migration of VMs and energy across different nodes. Compared to VM allocation alone, joint-optimization of VM and energy allocation reduces utility electricity consumption by up to 22%. This reduction can reach up to 28.5% for the same system when integrating less energy-efficient servers. The gains are demonstrated using simulation and a Mixed Integer Linear Programming formulation for the resource allocation problem. Furthermore, we show how our solution contributes to sustaining the energy consumption of old-generation and less efficient servers in mini data centers.

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Section: Optimization For Data Center Resource Allocationmentioning

confidence: 99%

Optimization of Data and Energy Migrations in Mini Data Centers for Carbon-Neutral Computing

Silva

Gamatié

Sassatelli

et al. 2023

IEEE Trans. Sustain. Comput.

View full text Add to dashboard Cite

show abstract

“…The separate prediction model was employed to predict resource utilization at a VM, meaning multiple predictor systems were installed at a single VM. Some applications of VM consolidation in cloud computing were provided in Reference 39. Table 1 illustrates the advantage and disadvantages of different virtual machine consolidation approaches.…”

Section: Literature Reviewmentioning

confidence: 99%

Confidence interval‐based overload avoidance algorithm for virtual machine placement

et al. 2022

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Virtualization plays an essential role in decreasing energy consumption and optimizing resource utilization by enabling the creation of virtual machines (VM) and their consolidation through live migration. Excessive migrations and a lack of required VMs are two critical factors in QoS degradation. The current consolidation approaches impose an intensive time complexity and cannot be used in large data centers with hundreds of hosts. This article proposes a framework for dynamic consolidation divided into a QoS‐aware algorithm for overload avoidance and a power‐aware algorithm for VM placement. To compute a safe zone criterion for any VM, relations were suggested by applying an interval estimate with a confidence level. By employing this criterion, the offered algorithm could guarantee the quality of service (QoS), particularly for specific VMs, while avoiding overhead. The VM placement algorithm is developed based on the maximum utilization of active hosts. It provides the capability to control the number of active hosts for the data center manager. The simulation results with real workloads revealed that the proposed framework could decline the amount of service level agreement violations by 78% and the number of migrations by 74%, and energy consumption by up to 13% in comparison with the best results of the benchmark algorithms. Hence, the application of this framework upgrades the QoS of data centers and declines their energy costs.

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“…Meta-heuristic algorithms are effective methods for finding optimal solutions for problems, but in contrast, heuristic algorithms are generally slower and need more time. 26 Mathematical and statistical approaches comprising linear programming, 21 dynamic programming, 27 regression, 28 and constraint satisfaction problems (CSP) 29 ; heuristic approaches comprising the first fit decreasing (FFD), 30 best fit decreasing (BFD), 31 MBFD 32 ; also, meta-heuristic approaches such as the genetic algorithm (GA), 33 ant colony optimization (ACO), 34 PSO, 35 and firefly optimization (FFO) 36 can be used in the VMC implementation.…”

Section: Related Workmentioning

confidence: 99%

An energy‐aware virtual machines consolidation method for cloud computing: Simulation and verification

et al. 2021

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Application of virtual machine consolidation in cloud computing systems

Cited by 17 publications

References 73 publications

Optimization of Data and Energy Migrations in Mini Data Centers for Carbon-Neutral Computing

Optimization of Data and Energy Migrations in Mini Data Centers for Carbon-Neutral Computing

Confidence interval‐based overload avoidance algorithm for virtual machine placement

An energy‐aware virtual machines consolidation method for cloud computing: Simulation and verification

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