scholarly journals Ismael: Using Machine Learning to Predict Acceptance of Virtual Clusters in Data Centers

2019 ◽  
Vol 16 (3) ◽  
pp. 950-964 ◽  
Author(s):  
Johannes Zerwas ◽  
Patrick Kalmbach ◽  
Stefan Schmid ◽  
Andreas Blenk
Keyword(s):  
Machine Learning ◽  
Data Centers ◽  
Virtual Clusters

scholarly journals Power consumption prediction in cloud data center using machine learning

2020 ◽  
Vol 10 (2) ◽  
pp. 1524 ◽  
Author(s):  
Deepika T. ◽  
Prakash P.
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Power Consumption ◽  
Data Center ◽  
Data Centers ◽  
Virtual Machines ◽  
Service Providers ◽  
Business World ◽  
Cloud Data ◽  
Cloud Data Centers

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.

scholarly journals A Cartesian Genetic Programming Based Parallel Neuroevolutionary Model for Cloud Server’s CPU Usage Prediction

Electronics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 67
Author(s):  
Qazi Zia Ullah ◽  
Gul Muhammad Khan ◽  
Shahzad Hassan ◽  
Asif Iqbal ◽  
Farman Ullah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Genetic Programming ◽  
Resource Utilization ◽  
Data Center ◽  
Data Centers ◽  
Electrical Energy ◽  
Learning Models ◽  
Cartesian Genetic Programming ◽  
Cloud Data ◽  
Machine Learning Models

Cloud computing use is exponentially increasing with the advent of industrial revolution 4.0 technologies such as the Internet of Things, artificial intelligence, and digital transformations. These technologies require cloud data centers to process massive volumes of workloads. As a result, the data centers consume gigantic amounts of electrical energy, and a large portion of data center electrical energy comes from fossil fuels. It causes greenhouse gas emissions and thus ensuing in global warming. An adaptive resource utilization mechanism of cloud data center resources is vital to get by with this huge problem. The adaptive system will estimate the resource utilization and then adjust the resources accordingly. Cloud resource utilization estimation is a two-fold challenging task. First, the cloud workloads are sundry, and second, clients’ requests are uneven. In the literature, several machine learning models have estimated cloud resources, of which artificial neural networks (ANNs) have shown better performance. Conventional ANNs have a fixed topology and allow only to train their weights either by back-propagation or neuroevolution such as a genetic algorithm. In this paper, we propose Cartesian genetic programming (CGP) neural network (CGPNN). The CGPNN enhances the performance of conventional ANN by allowing training of both its parameters and topology, and it uses a built-in sliding window. We have trained CGPNN with parallel neuroevolution that searches for global optimum through numerous directions. The resource utilization traces of the Bitbrains data center is used for validation of the proposed CGPNN and compared results with machine learning models from the literature on the same data set. The proposed method has outstripped the machine learning models from the literature and resulted in 97% prediction accuracy.

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