Power Consumption of Virtual Machine Live Migration in Clouds

Huang, Qiang; Gao, Fengqian; Wang, Rui; Qi, Zhengwei

doi:10.1109/cmc.2011.62

Cited by 88 publications

(66 citation statements)

References 9 publications

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“…However, in these works, there was no evaluation for the migration power cost, not to mention the increased delay within the virtualised server. The power cost can reach up to 32W in the source, and 10W in the destination server for each migrated VM [23]. If these numbers are multiplied by the number of migrated VMs, such a price would militate against the deployment of these methods.…”

Section: Related Workmentioning

confidence: 99%

Maximizing the Energy Efficiency of Virtualized C-RAN via Optimizing the Number of Virtual Machines

Alhumaima

Ahmed

Al-Raweshidy

2018

IEEE Trans. on Green Commun. Netw.

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Abstract-In cloud radio access networks (C-RAN), more accurate prediction of the number of virtual machines (VMs) one server can support would improve network capacity and energy efficiency (EE). In this paper, the problem of allocating an optimal number of VMs to the cloud server is introduced. Monte Carlo based evolutionary algorithm (PSO, QPSO or GA) are used to find the suboptimal number of VMs that optimises the energy efficiency (EE) of C-RAN. To enable such evaluation, a power model is proposed to evaluate the power consumption (PC) of each unit within a virtualised server. This evaluation occurs under the circumstances of increased number of hosted VMs, and processed resource blocks (RBs) at each VM. Moreover, power allocation methods are proposed to transmit the power from base band unit (BBU) pool to the remote radio heads (RRHs), and from RRHs to the users (UEs). This allocation is based on the combination of one or more of RRH distance, RRH channel gain, UE distance, UE channel gain, and UE path loss. The EE problem was constrained to crucial quality of service (QoS) indicators, including minimum UE data rate, number of allocated RBs, and latency imposed due to virtualisation.

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Section: Related Workmentioning

confidence: 99%

Maximizing the Energy Efficiency of Virtualized C-RAN via Optimizing the Number of Virtual Machines

Alhumaima

Ahmed

Al-Raweshidy

2018

IEEE Trans. on Green Commun. Netw.

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“…VM live migration is that in which a VM is transferred from a physical server to another while continuously running, without any noticeable effects from the point of view of end users [21]. Multiple VMs can be dynamically started and stopped on a physical machine providing elasticity and flexibility.…”

Section: A Vm Migrationmentioning

confidence: 99%

“…VM1 and VM2 consolidated on P2 and P3 respectively. And P1 is turned off, so the power consumption of the cluster is reduced [21].…”

Section: A Vm Migrationmentioning

confidence: 99%

Performance Evaluation of Power Aware VM Consolidation using Live Migration

Sehdev¹,

Kumar²

2015

IJCNIS

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Abstract-PowerEfficiency is the efficient use of power and is a crucial come forth in cloud computing environment. Green Computing is nothing but is a cloud computing with efficient use of power and green refers to make the environment friendly to the user by saving heat and power. Data centre power efficiency in cloud environment will be reduced when virtualization is used as contrary to physical resource deployment to book adequate to grant all application requests. Nevertheless, in any case of the resource provisioning approximation, occasion remains in the way in which they are made attainable and workload is scheduled. The objective of this research work is therefore to pack workload into servers, selected as a function of their cost to operate, to achieve (or as close to) the utmost endorsed employment in a cost-efficient manner, avoiding occurrences where devices are under-utilized and management cost is acquired inefficiently. This work has enhanced the existing work by introducing the dynamic wake up calls either to shut down the active servers or restart the passive server. The wakeup calls has been initiated dynamically. The overall objective is to decrease the response time of users which will be increased during wakeup time in existing research work.

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“…Among all activities, VM migration is one of the most widely used, because it provides the capability of moving the state of running VMs between physical machines, thus it allows to dynamically adjust the workload. Despite having a considerable impact on energy consumption [3], this activity has usually not been taken into account when building energy models for data centres and consolidation.…”

Section: Introductionmentioning

confidence: 99%

“…For example, [4] proposed a model based on network traffic generated by VM migration, while [3] focused on VM's CPU utilisation. However, past works focused on VMs only and have not considered other relevant actors involved in the migration process.…”

Section: Introductionmentioning

confidence: 99%

A Workload-Aware Energy Model for Virtual Machine Migration

Maio¹,

Kecskeméti²,

Prodan³

2015

2015 IEEE International Conference on Cluster Computing

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Abstract-Energy consumption has become a significant issue for data centres. Assessing their consumption requires precise and detailed models. In the latter years, many models have been proposed, but most of them either do not consider energy consumption related to virtual machine migration or do not consider the variation of the workload on (1) the virtual machines (VM) and (2) the physical machines hosting the VMs. In this paper, we show that omitting migration and workload variation from the models could lead to misleading consumption estimates. Then, we propose a new model for data centre energy consumption that takes into account the previously omitted model parameters and provides accurate energy consumption predictions for paravirtualised virtual machines running on homogeneous hosts. The new model's accuracy is evaluated with a comprehensive set of operational scenarios. With the use of these scenarios we present a comparative analysis of our model with similar state-of-the-art models for energy consumption of VM Migration, showing an improvement up to 24% in accuracy of prediction.

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Power Consumption of Virtual Machine Live Migration in Clouds

Cited by 88 publications

References 9 publications

Maximizing the Energy Efficiency of Virtualized C-RAN via Optimizing the Number of Virtual Machines

Maximizing the Energy Efficiency of Virtualized C-RAN via Optimizing the Number of Virtual Machines

Performance Evaluation of Power Aware VM Consolidation using Live Migration

A Workload-Aware Energy Model for Virtual Machine Migration

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