Estimating the Power Consumption of an Idle Virtual Machine

Quesnel, Flavien; Mehta, Hemant Kumar; Menaud, Jean-Marc

doi:10.1109/greencom-ithings-cpscom.2013.63

Cited by 15 publications

(8 citation statements)

References 23 publications

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“…Flavien et al, [13] explored the challenges, in a cloud environment, to diminish the power consumption of VMs and the operational expenses for cloud vendor. They implemented the ad-hoc framework for VM consolidation; but this approach did not take into account VM requirements like disk space, network bandwidth and time taken by VM to complete a particular task.…”

Section: Related Workmentioning

confidence: 99%

Power consumption prediction in cloud data center using machine learning

Deepika

Prakash

2020

IJECE

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The flourishing development of the cloud computing paradigm provides several services in the industrial business world. Power consumption by cloud data centers is one of the crucial issues for service providers in the domain of cloud computing. Pursuant to the rapid technology enhancements in cloud environments and data centers augmentations, power utilization in data centers is expected to grow unabated. A diverse set of numerous connected devices, engaged with the ubiquitous cloud, results in unprecedented power utilization by the data centers, accompanied by increased carbon footprints. Nearly a million physical machines (PM) are running all over the data centers, along with (5 – 6) million virtual machines (VM). In the next five years, the power needs of this domain are expected to spiral up to 5% of global power production. The virtual machine power consumption reduction impacts the diminishing of the PM’s power, however further changing in power consumption of data center year by year, to aid the cloud vendors using prediction methods. The sudden fluctuation in power utilization will cause power outage in the cloud data centers. This paper aims to forecast the VM power consumption with the help of regressive predictive analysis, one of the Machine Learning (ML) techniques. The potency of this approach to make better predictions of future value, using Multi-layer Perceptron (MLP) regressor which provides 91% of accuracy during the prediction process.

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Section: Related Workmentioning

confidence: 99%

Power consumption prediction in cloud data center using machine learning

Deepika

Prakash

2020

IJECE

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“…From that table, we can clearly see that MGAP and BCP-based MPDC are very close, both outperforming the NMSP scheme. [42] 60 watts Maximum Power (P max) [42] 275 watts λ 1 CP Load B (%) [42] 75 VM capacity L i (Mbps) 200, 210, .., 2190 Fixed capacity L 0 (Mbps) [43] 225 Cost of 4 VM types ($/hour) [13] [0.0475 0.0592 0.03545 0.1575]…”

Section: A Complexity Analysis 1) Comparison To Exhaustive Searchmentioning

confidence: 99%

Optimization of Virtualization Cost, Processing Power and Network Load of 5G Software-Defined Data Centers

Salhab

Rahim

Langar

2020

IEEE Trans. Netw. Serv. Manage.

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Virtualization is getting unprecedented attention from Mobile Network Operators (MNOs) as it provides agility in deployment, especially when coupled with the Cloud that offers inherent elasticity and load-balancing of resources. MNOs have to ensure operational excellence by meeting several objectives. In this context, we propose in this paper, a framework for optimizing the mapping of next Generation Node-Bs (gNBs) to Software-Defined 5G Core (5GC) delay tolerant Network Functions (NFs). These NFs are considered to be deployed as a Virtual Machine (VM) pool, or containers, in order to minimize cloud computing cost, processing power and at the same time maximize network load. First, we formulate this problem as an integer linear program, while taking into account multiple constraints including Virtual Central Processing Unit (vCPU) capacity, central processing load limits and integrality of mapping relations between gNBs and 5GC NFs. Then, we propose an algorithm to solve large problem instances based on Branch, Cut and Price (BCP) combining all of "Branch and Price", "Branch and Cut" and "Branch and Bound" frameworks. We present several schemes reflecting different optimization goals that the MNO can foster: virtualization cost, power minimization, network load or all. Simulation results demonstrate the good performance of our proposed algorithm to solve the gNBs-VM pool mapping for all evaluated schemes, while also emphasizing the advantages of a particular one (EWoS-333 for Equal Weight optimization Scheme) that can decrease virtualization cost by almost one order of magnitude compared to a static selection scheme, while considering the other two objectives.

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“…Unlike PMs, VMs energy consumption cannot be measured directly as they do not have direct hardware interfaces to plug in any of the wall Watts meters. Therefore, their energy information can be indirectly identified via software tools that model the energy consumed by the PMs in which they are hosted [34] with the use of different approaches, like resource usage-based [35], [13], [12,9] lookup table-based [36], and performance counters-based [8].…”

Section: Vm Energy Modelsmentioning

confidence: 99%

“…In terms of the PMs idle power consumption, most of the related work does not consider it or attributes it evenly to the VMs, which would not be fair when heterogeneous VMs are running alongside on the same PM. The only exception is the model presented in [35] which considers attributing the PMs idle power consumption to homogeneous and heterogeneous VMs; yet when part of the PMs CPU and memory resources are assigned to the VMs, it only attributes part of the PMs idle power to VMs, which is considered unfair as that given PM is switched on to run and maintain the status of the VMs; otherwise, that given PM could be switched off to save its idle power consumption. In terms of the PMs active power consumption, some of the related work models [36,35,12] attribute it to homogeneous VMs only.…”

Section: Vm Energy Modelsmentioning

confidence: 99%

“…The only exception is the model presented in [35] which considers attributing the PMs idle power consumption to homogeneous and heterogeneous VMs; yet when part of the PMs CPU and memory resources are assigned to the VMs, it only attributes part of the PMs idle power to VMs, which is considered unfair as that given PM is switched on to run and maintain the status of the VMs; otherwise, that given PM could be switched off to save its idle power consumption. In terms of the PMs active power consumption, some of the related work models [36,35,12] attribute it to homogeneous VMs only. The other models [8], [9] consider attributing the PMs active power to homogeneous and heterogeneous VMs, but using different approaches.…”

Section: Vm Energy Modelsmentioning

confidence: 99%

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