Power and Resource-Aware VM Placement in Cloud Environment

Garg, Neha; Singh, Damanpreet; Goraya, Major Singh

doi:10.1109/iadcc.2018.8692118

Cited by 12 publications

(5 citation statements)

References 12 publications

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“…Energy consumption in kilowatt-hours, the numeral of VMM, numeral of hosts shutdowns, and average SLA violation in percentage parameters are used to evaluate the performance presented algorithm. These parameters [18] are described below:…”

Section: Methodsmentioning

confidence: 99%

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VM Selection and Allocation Policy to Optimize VM Migration in Cloud Environment

Garg¹,

Singh²,

Goraya³

2019

IJRTE

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Cloud computing, a metered based technology provides the services using virtualized technology over the internet. In the cloud environment, to improve the performance (such as utilization of the resources, energy minimization) extreme number of virtual machines (VMs) can be installed on the servers as per their resource capacity. In this way, servers can be overloaded. Overloaded servers consume more energythan normal status servers. VM migration (VMM) is an efficient technique to become a server in a normal state. VMM technique is used to consolidate the resources to increase resource utilization (RU) and reduceenergy usage. In the VMM technique, selection of VM such as which VM is migrated from one server to another server and allocation of VM on servers is an important aspect. Appropriate VM selection declines the numeral of VMMs and increasesenergy efficiency. Appropriate VM allocation declines the server to become overloaded. In this paper, the VM selection and allocation strategy is presented. CloudSim toolkit is used to verify the strength of proposed VM selection and allocation algorithm. Proposed VM Selection algorithm (MaMT) performs better than existing MiMT algorithm in terms of total energy consumption, number of hosts shut down, number of VMM, and average Service Level Agreement (SLA) violation rate. MaMT algorithm with resource aware provisioning (RAP) and MiMT+RAP algorithm combines both VM selection and allocation policies. RAP algorithm used both energy and RU parameters while allocating VM to the server.MaMTreduces the energy consumption up to 7.25% and reduces the SLA violation rate up-to 2.6% in comparison to MiMT algorithm. When VM selection and allocation policies combines together than more system performance is improved. MaMT+RAPreduces the energy consumption up to6.76% and reduces the SLA violation rate up-to 0.22% in comparison to MaMT algorithm.MiMT+RAPreduces the energy consumption up to15.23% and reduces the SLA violation rate up-to 0.95% in comparison to MiMT algorithm.

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Section: Methodsmentioning

confidence: 99%

“…resources use the energy to execute the workload. Like paper [4,18], energy consumption of only CPU is considered. Equation 2 describes the total energy consumptionas:…”

Section: A Energy Consumptionmentioning

confidence: 99%

VM Selection and Allocation Policy to Optimize VM Migration in Cloud Environment

Garg¹,

Singh²,

Goraya³

2019

IJRTE

Self Cite

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“…(i) HP ProLiant ML110 G4 and HP ProLiant ML110 G5 are two types of hosts are deployed [28] (ii) The energy consumption rates of these two different types of hosts are 117 Watts per second ðWs −1 Þ and 135 Ws −1 [28] (iii) The energy consumption rate to transfer 1GB of data is 2.3 W [29] (iv) Four types of VM [19] On the arrival of a new workflow,it is analysed to get the type of the workflow, number of tasks, and dependency between them in the workflow When any task executes successfully then check its child tasks.…”

Section: Simulation Parametersmentioning

confidence: 99%

Energy-Efficient Scientific Workflow Scheduling Algorithm in Cloud Environment

Garg

Neeraj

Raj

et al. 2022

Wireless Communications and Mobile Computing

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Scheduling extensive scientific applications that are deadline-aware (usually referred to as workflow) is a difficult task. This research provides a virtual machine (VM) placement and scheduling approach for effectively scheduling process tasks in the cloud environment while maintaining dependency and deadline constraints. The suggested model’s aim is to reduce the application’s energy consumption and total execution time while taking into account dependency and deadline limitations. To select the VM for the tasks and dynamically deploy/undeploy the VM on the hosts based on the jobs’ requirements, an energy-efficient VM placement (EVMP) algorithm is presented. Demonstrate that the proposed approach outperforms the existing PESVMC (power-efficient scheduling and VM consolidation) algorithm.

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“…Minimizing the SLA violation along with minimizing the energy consumption is also a great matter of concern. Garg et al [37] proposed power and resourceaware VM placement algorithm to minimize the energy consumption, number of VM migrations, SLA violation rate, and number of server shutdowns. Han et al [38] proposed a VM consolidation algorithm to minimize energy consumption and SLA violation rate.…”

Section: Related Workmentioning

confidence: 99%

“…II. Total energy consumption: Total energy consumption defines the total CPU [37,84] energy consumption of the server to execute the simulation's application workload. In this paper, total energy consumption is measured in kilowatthour (kWh).…”

Section: Performance Metricsmentioning

confidence: 99%

Optimal virtual machine scheduling in virtualized cloud environment using VIKOR method

2021

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This paper presents a Power and Resource Utilization-Aware Virtual Machine Scheduling (PRUVMS) algorithm for strengthening resource utilization and diminishing the energy consumption of servers in the cloud environment. The PRUVMS algorithm enhances the resource utilization by migrating the VMs from the underloaded/overloaded servers to a normal server, and it reduces the energy consumption by shutting down the underloaded servers after migrating the VMs. For selecting the suitable server for the VM placement, the ranking of the available servers is evaluated. An illustrative example is presented to validate the PRUVMS algorithm. Further, the PRUVMS algorithm is tested on the PlanetLab workload using the Cloud-Sim simulator. The proposed PRUVMS algorithm improves resource utilization by 68.22% and 37.53% and decreases the energy consumption by 35.53% and 31.34% in comparison with PABFD and CAVMP algorithms, respectively. The improvement in computational results shows the acceptability of the proposed scheduling algorithm in the cloud environment.

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Power and Resource-Aware VM Placement in Cloud Environment

Cited by 12 publications

References 12 publications

VM Selection and Allocation Policy to Optimize VM Migration in Cloud Environment

VM Selection and Allocation Policy to Optimize VM Migration in Cloud Environment

Energy-Efficient Scientific Workflow Scheduling Algorithm in Cloud Environment

Optimal virtual machine scheduling in virtualized cloud environment using VIKOR method

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