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

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

doi:10.1002/spe.3010

Cited by 18 publications

(8 citation statements)

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“…Table 6 illustrate the generated random dataset specification. Multiple articles such as 18,51,52 have exploited the random workload for the evaluation of their approaches. The proposed framework, which is represented in Figure 5, was executed 20 times for each scenario.…”

Section: Simulation and Evaluation Resultsmentioning

confidence: 99%

“…In both papers, the host status is the criterion for detecting overhead status, and the host virtual machine monitor (VMM) workload is not considered. Zolfaghariet et al 18 have presented an energy‐aware and greedy method for the consolidation process. This study has shown algorithms to select the VM from the overloaded host and placement by considering multiple factors, including RAM, CPU, DISK, and BW.…”

Section: Literature Reviewmentioning

confidence: 99%

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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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Section: Simulation and Evaluation Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Confidence interval‐based overload avoidance algorithm for virtual machine placement

et al. 2022

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“…In Equation (19), P(u(t)) is the power consumption of host with CPU utilization u(t) at time t. The linear relationship between power consumption and CPU utilization can be modeled using Equation (20). In this equation, P idle is the power consumption of a host with the least processor utilization, which is the host that is turned on, with no virtual machine or with some unloaded virtual machines, and P max indicates the power consumption of a host with maximum processor utilization.…”

Section: Energy Consumption Modelingmentioning

confidence: 99%

“…Moreover, TOPSIS was proposed by Arianian et al 18 using the predicted values for these two criteria. In addition to the CPU productivity, other sources such as main memory, disk, and bandwidth are the criteria for their innovative selection method 19 …”

Section: Introductionmentioning

confidence: 99%

“…In addition to the CPU productivity, other sources such as main memory, disk, and bandwidth are the criteria for their innovative selection method. 19 In this article, we propose a different approach for the VM selection process. In addition to using migration time, four new criteria for the selection process are suggested: migration risk, virtual machine connectivity, releasable resources, and penalty for SLA violation.…”

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A flexible approach for virtual machine selection in cloud data centers with AHP

et al. 2021

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Increasing resource efficiency and reducing the energy consumption of cloud data centers is critical, especially during the global CORONA virus pandemic.Virtual machines' consolidation using live migration maximizes the hosts' and the reduction of energy consumption. An increase in the host's virtual machines in the consolidation process and the dynamic workload of the virtual machines may cause the overloading in the hosts. One approach to overcome this problem is reducing the hosts' virtual machines. One crucial issue to improve the quality of the consolidation process's quality is determining the best virtual machine for the migration process. Although the selection process has lower computational complexity than other challenges (like placement and overload prediction) in the consolidation process, this issue has received less attention. This article aims to present an efficient algorithm for the selection process. We first considered five main criteria for the selection process: migration time, migration risk, virtual machine connectivity, releasable resources, and penalty for SLA violation. Then, we propose an algorithm based on analytic hierarchy process multi-criteria decision-making technique. Next, to determine the weight of the proposed criteria, we simulate thousands of virtual machines of the PlanetLab workloads. These weights are tunable based on the data center preferences. The results of the suggested approach results show 23% reduction in the hosts' energy consumption, 49% reduction in the number of migrations, and 18% reduction in the SLA violation compared with other techniques. So, using the proposed method may significantly reduce the overall cost of the data centers. K E Y W O R D Sanalytic hierarchy process, cloud computing, dynamic consolidation, green cloud, MADA, virtualization INTRODUCTIONRecently, organizations, research centers, and businesses paid more attention to the advantage of using cloud data centers. These organizations prefer to use the cloud data centers to invest in private data centers and reduce the challenges and cost of their data centers. 1 So, many large companies like Amazon, Google, and Microsoft expand their large cloud data centers, which contain thousands of hosts. One of the main concerns of the data centers is the large amount of energy 1216

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CRACLE: Customer Resource Allocation in CLoud Environment

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Johari²,

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et al. 2023

Proceedings of Data Analytics and Management

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

Cited by 18 publications

References 50 publications

Confidence interval‐based overload avoidance algorithm for virtual machine placement

Confidence interval‐based overload avoidance algorithm for virtual machine placement

A flexible approach for virtual machine selection in cloud data centers with AHP

CRACLE: Customer Resource Allocation in CLoud Environment

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