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

Ahmadi, Javad; Haghighat, Abolfazl Toroghi; Rahmani, Amir Masoud; Ravanmehr, Reza

doi:10.1002/spe.3062

Cited by 12 publications

(9 citation statements)

References 52 publications

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“…Najafizadegan et al 24 have used learning automata to determine the VM in a different method. In addition, Ahmadi et al 25 introduce four novel criteria for VM selection with an AHP‐based algorithm. The MMT approach can be easily implemented.…”

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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Section: Literature Reviewmentioning

confidence: 99%

Confidence interval‐based overload avoidance algorithm for virtual machine placement

et al. 2022

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“…The Equation (11) represents the target relationship between CPU workload and power consumption that we aimed to establish. By using this equation, the distribution and density of the power p can be derived from the distribution and density of any given workload w. As demonstrated above, F(p) can be given in terms of P{p ≤ p} = P{g(w) ≤ p};…”

Section: Model Design and Implementationmentioning

confidence: 99%

“…Currently, there is a plenty of approaches aiming at ensuring energy efficiency, including scheduling, 9,10 consolidation, 11,12 and load balancing 13 . These techniques require a comprehension of the different cost aspects.…”

Section: Introductionmentioning

confidence: 99%

“…6 Therefore, the consumption of power by servers is the primary driver of energy and heat flow in data centers, and many researchers have developed approaches over the last years to rapidly increase the energy efficiency of these components, including the scaling, scheduling, and consolidation technologies. 7,8 Currently, there is a plenty of approaches aiming at ensuring energy efficiency, including scheduling, 9,10 consolidation, 11,12 and load balancing. 13 These techniques require a comprehension of the different cost aspects.…”

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Modeling the correlation between the workload and the power consumed by a server using stochastic and non‐parametric approaches

2022

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In this article, we address a critical concern of the growth of computer servers number and the resulting power expenditures in data centers by analyzing the statistical metrics related to the workload executed in each physical node. The aim is to build a stochastic model for power consumption estimation based on historical data. Relying on in-depth investigation and experimental testing of the power consumption features and performance of the various workload datasets, we propose a model that considers the workload and the power consumed to be executed by a server as random variables. Based on the properties of the probabilistic distribution function of each random variable, we establish the correlation relationship between the workload and the power consumption using a non-parametric approach. Our use of a non-parametric method to learn a given probability model is challenging because it requires estimating the full distribution from the available data samples. The accuracy of our approach is demonstrated by estimating the energy consumption of various workloads. The experimental and simulation results show that our model outperforms many existing approaches in terms of accuracy, and it can be applied to a wide variety of workloads.

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“…e.g., in Italy country social distancing was forcible during the COVID-19 and Microsoft team service faced 775% growth in calling users monthly. Also, many IT large companies such as Amazon, Google and Microsoft have extend their's clouds, which consist thousands of PM [9].…”

Section: Motivation and Objectivesmentioning

confidence: 99%

A prediction and fuzzy-based approach for VM migration for optimal trade off energy-performance in cloud network

Zolfaghari

2022

Preprint

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Energy efficiency, low utilization of Physical Machines (PM) and service access level are challenges of cloud networks, while efficient migration of Virtual Machines (VMs) is a solution for them. Uncontrolled migration of the VMs can causes migration overhead, that most important of them comprising: violation in Service Level Agreement (SLA) and high energy consumption. Generally, performance requirements in the form of SLAs are formalized. The main goal of the dynamic migration of VMs is a trade-off in energy and performance. Detecting when cloud's PMs is being overloaded or under-loaded are two substantial sub-problems of dynamic VM migration, which directly effects the performance and energy efficiency. Thus, migration technique is a practical approach for balancing the electricity consumption and Quality of Service (QoS) requirements in the cloud networks. In this paper, with definition an architectural framework in the system modelling, in first to detect overloaded and under-loaded PMs a Predict Detection method (PD method), and then for selection of VMs on overloaded PMs a Fuzzy Selection method (FS method), in overall, algorithm (PDFS) is proposed. The algorithm PDFS simulation results demonstrate that the proposed algorithm significantly outperforms benchmark algorithms in terms of energy, migration overhead, service access and SLA violation.

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

Cited by 12 publications

References 52 publications

Confidence interval‐based overload avoidance algorithm for virtual machine placement

Confidence interval‐based overload avoidance algorithm for virtual machine placement

Modeling the correlation between the workload and the power consumed by a server using stochastic and non‐parametric approaches

A prediction and fuzzy-based approach for VM migration for optimal trade off energy-performance in cloud network

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