Bibliometric Analysis of Data Analytics Techniques in Cloud Computing Resources Allocation

  • Sello Prince Sekwatlakwatla North-West University, South Africa
  • Vusumuzi Malele North-West University, South Africa
DOI: 10.51519/journalisi.v6i3.782
Keywords: Resource allocation, Data Analytics Techniques, Traffic prediction

Abstract

Cloud computing provides on-demand computing services over the Internet, allowing for quicker innovation, more flexible resources, and economies of scale while reducing the need for physical data centers and servers. With this benefit, most organizations are adopting this technology, and some organizations are also operating fully on cloud computing. This causes traffic to increase, and most of these organizations are struggling with resource allocation, resulting in complaints from users regarding inactive system performance, timeouts in applications, and higher bandwidth use during peak hours. In this regard, this study investigates data analytics techniques and tools for the allocation of resources in cloud computing. The study indexed journal articles from the Scopus Database and Web of Science (WOS) between 2010 and 2024. This article brings new insights into the analysis of data analytics techniques in Africa and collaborations with other developing countries. The findings present tools and approaches that may be used to allocate cloud computing resources and give recommendations.

Downloads

Download data is not yet available.

References

P. Sharma and V. Jadhao, "Molecular Dynamics Simulations on Cloud Computing and Machine Learning Platforms," Proc. 2021 IEEE 14th Int. Conf. Cloud Comput. (CLOUD), Chicago, IL, USA, pp. 751-753, 2021.

G. Peng, H. Wu, H. Wu, and K. Wolter, "Constrained Multiobjective Optimization for IoT-Enabled Computation Offloading in Collaborative Edge and Cloud Computing," in IEEE Internet Things J., vol. 8, no. 17, pp. 13723-13736, 2021.

P. Kumar, A. Tharad, U. Mukhammadjonov, and S. Rawat, "Analysis on Resource Allocation for Parallel Processing and Scheduling in Cloud Computing," Proc. 2021 5th Int. Conf. Inf. Syst. Comput. Networks (ISCON), Mathura, India, pp. 1-6, 2021.

J. Kamiri and G. Mariga, “Research Methods in Machine Learning: A Content Analysis," Int. J. Comput. Inf. Technol., vol. 10, no. 2, 2021.

I. Obeidat and A. Mughaid, "Implementing Factors of Information Security in Governmental Organizations of Jordan," Proc. Thirteenth Int. Conf. Digital Soc. eGovt., 2019.

T. Ahmad and N. Zhou, "Ensemble Methods for Probabilistic Solar Power Forecasting: A Comparative Study," Proc. 2023 IEEE Power Energy Soc. Gen. Meeting (PESGM), Orlando, FL, USA, pp. 1-5, 2023.

T. Qin, G. Cheng, Y. Wei, and Z. Yao, "Hier-SFL: Client-Edge-Cloud Collaborative Traffic Classification Framework Based on Hierarchical Federated Split Learning," J. Future Gener. Comput. Syst., vol. 149, 2023. doi: 10.1016/j.future.2023.07.001

Z. Zhao et al., "Research on Edge Cloud Architecture of Intelligent Power Plant," Proc. 2022 IEEE 2nd Int. Conf. Digital Twins Parallel Intell. (DTPI), Boston, MA, USA, pp. 1-3, 2022.

N. Mohan and V. Jain, "Performance Analysis of Support Vector Machine in Diabetes Prediction," Proc. 2020 4th Int. Conf. Electron. Commun. Aerosp. Technol. (ICECA), Coimbatore, India, pp. 1-3, 2020.

N. H. Haay and M. N. N. Sitokdana, “Analysis of Information Technology Governance on Communication and Information Service of Papua Province Using COBIT 2019,” J. Inf. Syst. Informatics, vol. 4, no. 2, pp. 349–360, 2022.

K. Ntanzi, A. K. Lumbwe, S. C. Mukwakungu, and N. Sukdeo, "The Relationship Between the Implementation of Quality Management Practices and Service Quality in the South African Financial Service Industry," Proc. 2020 IEEE Int. Conf. Ind. Eng. Eng. Manage. (IEEM), Singapore, pp. 1235-1240, 2020.

M. Hao et al., "Artificial Neural Network-Based Approach to Modeling Energy Bands of GaN-Based Heterojunction Materials," Proc. 2023 Int. Conf. High Perform. Big Data Intell. Syst. (HDIS), Macau, China, pp. 71-76, 2023.

S. Arlis and S. Defit, “Machine Learning Algorithms for Predicting the Spread of Covid-19 in Indonesia,” TEM J., vol. 10, no. 2, pp. 970–974, 2021.

Y. Sun and J. Zhu, "Forecasting Chinese Electric Vehicle Volume Using Keyword Search Data Based on SARIMA-LSTM Model," Proc. 2023 8th Int. Conf. Inf. Syst. Eng. (ICISE), Dalian, China, pp. 467-472, 2023.

S. K. Panigrahi, "Forecasting Twitter's Favorite Cryptocurrencies Based on a Comparative Assessment of SARIMA, LSTM, and Fb Prophet Machine Learning Approach," Proc. 2023 Int. Conf. Sustain. Islamic Bus. Finance (SIBF), Bahrain, pp. 42-45, 2023.

P. Kumar, A. Tharad, U. Mukhammadjonov, and S. Rawat, "Analysis on Resource Allocation for Parallel Processing and Scheduling in Cloud Computing," Proc. 2021 5th Int. Conf. Inf. Syst. Comput. Networks (ISCON), Mathura, India, pp. 1-6, 2021.

X. Mou and H. Chen, "Network Information Resource Allocation under Big Data Technology," Proc. 2023 IEEE 2nd Int. Conf. Electr. Eng. Big Data Algorithms (EEBDA), Changchun, China, pp. 15-19, 2023.

Published
2024-09-26
Abstract views: 74 times
Download PDF: 67 times
How to Cite
Sekwatlakwatla, S. P., & Malele, V. (2024). Bibliometric Analysis of Data Analytics Techniques in Cloud Computing Resources Allocation. Journal of Information Systems and Informatics, 6(3), 2037-2047. https://doi.org/10.51519/journalisi.v6i3.782
Issue
Vol 6 No 3 (2024): Journal of Information Systems and Informatics
Section
Articles

Copyright (c) 2024 Journal of Information Systems and Informatics

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

  1. I certify that I have read, understand and agreed to the Journal of Information Systems and Informatics (Journal-ISI) submission guidelines, policies and submission declaration. Submission already using the provided template.
  2. I certify that all authors have approved the publication of this and there is no conflict of interest.
  3. I confirm that the manuscript is the authors' original work and the manuscript has not received prior publication and is not under consideration for publication elsewhere and has not been previously published.
  4. I confirm that all authors listed on the title page have contributed significantly to the work, have read the manuscript, attest to the validity and legitimacy of the data and its interpretation, and agree to its submission.
  5. I confirm that the paper now submitted is not copied or plagiarized version of some other published work.
  6. I declare that I shall not submit the paper for publication in any other Journal or Magazine till the decision is made by journal editors.
  7. If the paper is finally accepted by the journal for publication, I confirm that I will either publish the paper immediately or withdraw it according to withdrawal policies
  8. I Agree that the paper published by this journal, I transfer copyright or assign exclusive rights to the publisher (including commercial rights)