A Cartesian Genetic Programming Based Parallel Neuroevolutionary Model for Cloud Server's CPU Usage Prediction
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- @Article{ullah:2021:Electronics,
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author = "Qazi Zia Ullah and Gul Muhammad Khan and
Shahzad Hassan and Asif Iqbal and Farman Ullah and
Kyung Sup Kwak",
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title = "A Cartesian Genetic Programming Based Parallel
Neuroevolutionary Model for Cloud Server's {CPU} Usage
Prediction",
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journal = "Electronics",
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year = "2021",
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volume = "10",
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number = "1",
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keywords = "genetic algorithms, genetic programming, cartesian
genetic programming",
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ISSN = "2079-9292",
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URL = "https://www.mdpi.com/2079-9292/10/1/67",
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DOI = "doi:10.3390/electronics10010067",
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abstract = "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 mechanism of cloud data center
resources is vital to get by with this huge problem.
The adaptive system will estimate the resource use and
then adjust the resources accordingly. Cloud resource
use 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 use 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 97percent prediction
accuracy.",
-
notes = "also known as \cite{electronics10010067}",
- }
Genetic Programming entries for
Qazi Zia Ullah
Gul Muhammad Khan
Shahzad Hassan
Asif Iqbal
Farman Ullah
Kyung Sup Kwak
Citations