A Tree Regression-Based Approach for VM Power Metering

Gu, Chonglin; Shi, Pengzhou; Sheng, Shi; Huang, Hejiao; Jia, Xiaohua

doi:10.1109/access.2015.2430276

Cited by 19 publications

(27 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Gu et al [23] has proposed solidification as a new method for energy saving in cloud data centers. One of the significant downsides of current investigations on solidification solution is that they concentrate just around one criterion and disregard different ones.…”

Section: Related Work With Discussionmentioning

confidence: 99%

Novel load balancing approach based on ant colony optimization technique in cloud computing

Alyouzbaki

AL-Rawi

2021

Bulletin EEI

View full text Add to dashboard Cite

The cloud is the framework in which communication is connected with virtual machines, data centers, hosts, and brokers. The broker searches for a highly reliable cloudlet virtual machine for execution. Vulnerability can occur in the network because of which framework gets overburden. A research strategy is introduced in this article to expand the fault tolerance of the framework. The proposed approach improvement depends on the algorithm of ant colony optimization (ACO) that can choose the better virtual machine on which is to migrate the cloudlet to reduce the execution time and energy consumption. The efficiency of the proposed approach simulated in terms of execution time, energy consumption and examined with CloudSim. The introduction is provided in this article with a detailed description of cloud computing and, in addition, green cloud computing with its models. This article also discussed the virtual machine (VM) in more depth in the introduction section, which allows cloud service providers to supervise cloud resources competently while dispensing with the need for human oversight. Then the article submitted and explained the related works with their discussion and then it explained the novel proposed load balancing based on ACO technique and concluded that the execution time and energy consumption of the proposed technique is better than the three-threshold energy saving algorithm (TESA) technique that is commonly used in cloud load balancing.

show abstract

Section: Related Work With Discussionmentioning

confidence: 99%

Novel load balancing approach based on ant colony optimization technique in cloud computing

Alyouzbaki

AL-Rawi

2021

Bulletin EEI

View full text Add to dashboard Cite

show abstract

“…The data samples used in this method was less for training to learn the relationship between attributes. Chonglin et al, [10] presented a Tree Regression(TR)-based model to compute the VM power utilization, using cross-validation, based on black box method. The VM and server feature information are gathered based on black box method.…”

Section: Related Workmentioning

confidence: 99%

Power consumption prediction in cloud data center using machine learning

Deepika

Prakash

2020

IJECE

View full text Add to dashboard Cite

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.

show abstract

“…They are usually based on counters (hardware or software) in order to monitor the resource usage. Their accuracy thus depends which resources are selected, how they are monitored and which formulas are used to estimate the VM power consumption from the monitoring data, such as linear regression (Kim et al, 2011) (Wu et al, 2016), polynomial regression (Xiao et al, 2013), machine learning (Yang et al, 2014) or tree regression based approach (Gu et al, 2015). In these studies, estimation errors typically fluctuate from 2 to 5%.…”

Section: Related Workmentioning

confidence: 99%

A CO2 Emissions Accounting Framework with Market-based Incentives for Cloud Infrastructures

Margery

Guyon²,

Orgerie

et al. 2017

Proceedings of the 6th International Conference on Smart Cities and Green ICT Systems

View full text Add to dashboard Cite

Abstract:CO2 emissions related to Cloud computing reach nowadays worrying levels, without any reduction in sight. Often, Cloud users, asking for virtual machines, are not aware of such emissions which concern the entire Cloud infrastructures and are thus difficult to split into the actual resources utilization, such as virtual machines. We propose a CO2 emissions accounting framework giving flexibility to the Cloud providers, predictability to the users and allocating all the carbon costs to the users. This paper shows the architecture of our accounting framework and ideas on how to practically implement it.

show abstract

A Tree Regression-Based Approach for VM Power Metering

Cited by 19 publications

References 22 publications

Novel load balancing approach based on ant colony optimization technique in cloud computing

Novel load balancing approach based on ant colony optimization technique in cloud computing

Power consumption prediction in cloud data center using machine learning

A CO2 Emissions Accounting Framework with Market-based Incentives for Cloud Infrastructures

Contact Info

Product

Resources

About