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Article

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

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Department of Computer Engineering, Bahria University Islamabad, Islamabad 44000, Pakistan
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Electrical and Computer Engineering Department, COMSATS University Islamabad Attock Campus, Punjab 43600, Pakistan
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Intelligent System Design Group, National Centre of AI-UETP, University of Engineering and Technology, Peshawar 25120, Pakistan
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Department of Information and Communication Engineering, Inha University, Incheon 22212, Korea
*
Authors to whom correspondence should be addressed.
Electronics 2021, 10(1), 67; https://doi.org/10.3390/electronics10010067
Received: 17 November 2020 / Revised: 17 December 2020 / Accepted: 23 December 2020 / Published: 1 January 2021
(This article belongs to the Special Issue Applications for Smart Cyber Physical Systems)
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. View Full-Text
Keywords: cloud computing; cloud server; computations complexity; cartesian genetic programming; evolutionary algorithms; genetic programming; graph-based search; machine learning; neural networks; workload prediction cloud computing; cloud server; computations complexity; cartesian genetic programming; evolutionary algorithms; genetic programming; graph-based search; machine learning; neural networks; workload prediction
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MDPI and ACS Style

Ullah, Q.Z.; Khan, G.M.; Hassan, S.; Iqbal, A.; Ullah, F.; Kwak, K.S. A Cartesian Genetic Programming Based Parallel Neuroevolutionary Model for Cloud Server’s CPU Usage Prediction. Electronics 2021, 10, 67. https://doi.org/10.3390/electronics10010067

AMA Style

Ullah QZ, Khan GM, Hassan S, Iqbal A, Ullah F, Kwak KS. A Cartesian Genetic Programming Based Parallel Neuroevolutionary Model for Cloud Server’s CPU Usage Prediction. Electronics. 2021; 10(1):67. https://doi.org/10.3390/electronics10010067

Chicago/Turabian Style

Ullah, Qazi Zia, Gul Muhammad Khan, Shahzad Hassan, Asif Iqbal, Farman Ullah, and Kyung Sup Kwak. 2021. "A Cartesian Genetic Programming Based Parallel Neuroevolutionary Model for Cloud Server’s CPU Usage Prediction" Electronics 10, no. 1: 67. https://doi.org/10.3390/electronics10010067

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