An autonomous model for self‐optimizing virtual machine selection by learning automata in cloud environment

Najafizadegan, Negin; Nazemi, Eslam; Khajehvand, Vahid

doi:10.1002/spe.2960

Cited by 9 publications

(10 citation statements)

References 56 publications

(85 reference statements)

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“…All these methods have been used in different research studies. 17 The fourth scenario based on the idea given in Reference 59 is that the higher the correlation between applications that use the same resources on an oversubscribed server, the higher the probability of the server becoming overloaded. According to this idea, Beloglazov and Buyya 12 proposed the MC method.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Furthermore, the third scenario chooses the virtual machine with the lowest CPU utilization. All these methods have been used in different research studies 17 . The fourth scenario based on the idea given in Reference 59 is that the higher the correlation between applications that use the same resources on an oversubscribed server, the higher the probability of the server becoming overloaded.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…Therefore, in addition to the number of migration and energy consumption, the criteria mentioned in Equations () are selected efficiency evaluations. These criteria have been used in many research studies such as References 12,17,34,42. ECSVM can be a criterion for calculating Equation () by considering energy consumption, migration number, and SLA violation due to overload (SLATAH or SLA violation time per active host).…”

Section: Performance Evaluationmentioning

confidence: 99%

“…12 However, other reports have used the ratio of these two criteria 15 or first sorts migrant virtual machines in an ascending list based on their CPU utilization, and then rearranges them in an ascending list based on their memory. 16 Najafizadegan et al, 17 using these two criteria and the predicted value for the processor efficiency, suggested learning automata for the selection process. Moreover, TOPSIS was proposed by Arianian et al 18 using the predicted values for these two criteria.…”

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confidence: 99%

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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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Section: Simulation Resultsmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

mentioning

confidence: 99%

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

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“…In Reference 16, the VMs with the highest processor efficiency is selected for migration from overloaded hosts. 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.…”

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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Efficient Auto‐scaling for Host Load Prediction through VM migration in Cloud

Verma,

Bala

2023

Concurrency and Computation

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SummaryThe expeditious deployment of Cloud applications and services on wide‐ranging Cloud Data Centres (CDC) gives rise to the utilization of many resources. Moreover, by the increase in resource utilization, virtualization also greatly impacts achieving desired performance. The major challenges in virtualization are detecting over‐utilized or under‐utilized hosts at the right time and the proper scaling of Virtual Machines (VM) on the accurate host. Auto‐scaling in Cloud Computing allows the service providers to scale up or down the resources automatically and provides on‐demand computing power and storage capacities. Effective utilization and autonomous scaling of resources eventually reduce the load, energy consumption, and operating costs. In this paper, an efficient auto‐scaling approach for predicting host load through VM migration has been proposed. The ensemble method using different time‐series forecasting models has been proposed to forecast the approaching workload on the host. Based on this predicted load, different algorithms have been devised to detect over‐utilized and under‐utilized hosts and VMs can be migrated. The designed approach has been validated by experimentation on a real‐time Google cluster dataset. The proposed technique significantly improves average CPU utilization and reduces over‐utilization and under‐utilization. It also minimizes response time, service level agreement violations, and the slighter number of migrations and scaling overhead.

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An autonomous model for self‐optimizing virtual machine selection by learning automata in cloud environment

Cited by 9 publications

References 56 publications

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

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

Confidence interval‐based overload avoidance algorithm for virtual machine placement

Efficient Auto‐scaling for Host Load Prediction through VM migration in Cloud

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