Improving Energy Efficiency for Mobile Media Cloud via Virtual Machine Consolidation

Dong, Yi; Zhou, Liang; Jin, Yichao; Wen, Yonggang

doi:10.1007/s11036-015-0595-2

Cited by 10 publications

(3 citation statements)

References 31 publications

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“…From the perspective of energy consumption model in data centers, most of existing researches proposed their energy consumption models by few influencing factors such as CPU utilization rate, disk utilization rate and VM live migration. The author [14][15][16][17][18][19][20][21][22] formulated energy consumption as the linear energy consumption model which assumes that energy consumption of a PM increase linearly as CPU utilization rate increases. The author 13 formulated energy consumption of PM as the linear energy consumption model which considers the energy consumption by CPU and the energy consumption caused by VM live migration.…”

Section: Related Workmentioning

confidence: 99%

Energy‐aware virtual machine consolidation based on evolutionary game theory

Xia-lin

Chen

et al. 2022

Concurrency and Computation

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Cloud data center consumes enormous amount of electrical power resulting in high operating cost and carbon dioxide emission. Virtual machine (VM) consolidation strives to reallocate VMs to the minimum number of physical machines dynamically to reduce energy consumption. An approach is proposed to solve consolidation problem of VMs toward optimization of their energy consumption. A novel algorithm is presented, and an energy consumption model is also presented to support the algorithm to find optimal solutions. Experimental results show that energy consumption can be reduced greatly by dynamic VM consolidation. In comparison with other classic algorithms, our proposed algorithm outcomes them and achieves average savings of 42% on energy consumption.

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Section: Related Workmentioning

confidence: 99%

Energy‐aware virtual machine consolidation based on evolutionary game theory

Xia-lin

Chen

et al. 2022

Concurrency and Computation

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“…In fact, by moving services to VMs, the CPU utilization of that physical machine (PM) is increased. Then, the number of running PMs can be reduced and this decreases energy consumption [14].…”

Section: Review Of Research Efforts In Cloud Computing Data Center Enmentioning

confidence: 99%

An Adaptive Autonomic Framework for Optimizing Energy Consumption in the Cloud Data Centers

Diouani¹,

Medromi²

2019

IJIES

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Given the development of the Cloud Computing recently, clients and customers using the Cloud for both individual and business needs have expanded to an uncommon scale. This has normally prompted the expanded deployments of Cloud data centers over the globe. As a result, Cloud data centers are seen to be monstrous energy consumers and natural polluters. They require an extraordinary measure of regular energy which has made an effect on the energy supply and natural conditions of the environment. This is the reason why the vulnerability of persistent energy supply, later on, is being referred to. In this way, there is a need of an energy-aware cloud-based system which automatically and efficiently manages and optimize cloud computing data center resources by considering energy consumption as an essential Quality of Service (QoS) parameter. This paper, focus on the energy utilization of the data centers and how this can be limited so as to make the cloud computing greener. Thus, a new autonomic resource optimization manager has been proposed to avail the most optimum level of resources with reduced server energy consumption. The proposed framework has been verified theoretically and tested experimentally. The experimental analysis has demonstrated that the effectiveness of the proposed solution is greater than the state-ofthe-art methods in terms of the achieved results related to reducing energy consumption and response time in cloud computing data centers.

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“…Compared with the state-of-the-art suggested at work, the proposal achieves 23% energy savings. Dong et al (2015) proposed an algorithm that scales in a multidimensional manner the VMs on a homogeneous mobile cloud depending on two factors: the rate of CPU usage and the bandwidth among available hosts. Based on previous analysis, the minimum energy consumption and the number of physical machines in operation are derived.…”

Section: Related Workmentioning

confidence: 99%