Cloud Computing Research (2010–2026): A Scopus-Based Bibliometric Analysis of Intellectual Structure and Topic Shifts
DOI:
https://doi.org/10.58812/wsist.v4i01.2807Keywords:
Cloud Computing, Topic Evolution, Scopus, Bibliometric Analysis, VOSviewerAbstract
The research aims at mapping the evolution of cloud computing studies from 2010 to 2026 through the analysis of academic literature using bibliometrics. This approach will provide information on the structure of the knowledge, major contributions, and the thematic trends within the area. In the present work, the bibliometric analysis is based on Scopus indexation. The research uses several types of analysis: co-authorship, citation, and keyword co-occurrence analyses. VOSviewer was chosen as the main bibliographic software for data mining. The analysis shows that the field is dominated by major researchers representing countries including the USA, China, and India, which indicates a partially fragmented cooperation among experts in the domain. Bibliometric analysis through citation shows that research papers in cloud computing architecture, Internet of things, and edge computing play a crucial role, as they set the basis of the topic and show the trend toward decentralization and integration in computing. In addition, the multidimensionality of research in cloud computing was analyzed through keywords, which revealed not only technical issues but also security, applications, green computing, and energy efficiency. This study contributes to the literature by providing a systematic overview of the development and current landscape of cloud computing research, offering valuable insights for researchers and practitioners in identifying future research directions and opportunities for interdisciplinary collaboration.
References
[1] S. Sawhney, K. Kacker, S. Jain, S. N. Singh, and R. Garg, “Real-time smart attendance system using face recognition techniques,” in 2019 9th international conference on cloud computing, data science & engineering (Confluence), IEEE, 2019, pp. 522–525.
[2] O. Jayeola, S. Sidek, A. Abd Rahman, A. S. B. Mahomed, and J. Hu, “Cloud computing adoption in small and medium enterprises (SMEs): A systematic literature review and directions for future research,” Int. J. Bus. Soc., vol. 23, no. 1, pp. 226–243, 2022.
[3] S. El Kafhali, I. El Mir, and M. Hanini, “Security threats, defense mechanisms, challenges, and future directions in cloud computing,” Arch. Comput. Methods Eng., vol. 29, no. 1, pp. 223–246, 2022.
[4] S. Kanungo, “REVOLUTIONIZING DATA PROCESSING: ADVANCED CLOUD COMPUTING AND AI SYNERGY FOR IOT INNOVATION,” Int. Res. J. Mod. Eng. Technol. Sci., vol. 2, pp. 1032–1040, 2020.
[5] U. O. Matthew, J. S. Kazaure, and N. U. Okafor, “Contemporary development in E-Learning education, cloud computing technology & internet of things.,” EAI Endorsed Trans. Cloud Syst., vol. 7, no. 20, p. e3, 2021.
[6] J. Qin and Q. Qin, “Cloud platform for enterprise financial budget management based on artificial intelligence,” Wirel. Commun. Mob. Comput., vol. 2021, no. 1, p. 8038433, 2021.
[7] M. S. Hasan, F. A. de Oliveira, T. Ledoux, and J.-L. Pazat, “Enabling green energy awareness in interactive cloud application,” in 2016 IEEE International Conference on Cloud Computing Technology and Science (CloudCom), IEEE, 2016, pp. 414–422.
[8] N. Donthu, S. Kumar, D. Mukherjee, N. Pandey, and W. M. Lim, “How to conduct a bibliometric analysis: An overview and guidelines,” J. Bus. Res., vol. 133, pp. 285–296, 2021.
[9] N. Van Eck and L. Waltman, “Software survey: VOSviewer, a computer program for bibliometric mapping,” Scientometrics, vol. 84, no. 2, pp. 523–538, 2010.
[10] J. Gubbi, R. Buyya, S. Marusic, and M. Palaniswami, “Internet of Things (IoT): A vision, architectural elements, and future directions,” Futur. Gener. Comput. Syst., vol. 29, no. 7, pp. 1645–1660, 2013.
[11] A. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, and M. Ayyash, “Internet of things: A survey on enabling technologies, protocols, and applications,” IEEE Commun. Surv. tutorials, vol. 17, no. 4, pp. 2347–2376, 2015.
[12] W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu, “Edge computing: Vision and challenges,” IEEE internet things J., vol. 3, no. 5, pp. 637–646, 2016.
[13] Y. Mao, C. You, J. Zhang, K. Huang, and K. B. Letaief, “A survey on mobile edge computing: The communication perspective,” IEEE Commun. Surv. tutorials, vol. 19, no. 4, pp. 2322–2358, 2017.
[14] R. N. Calheiros, R. Ranjan, A. Beloglazov, C. A. F. De Rose, and R. Buyya, “CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms,” Softw. Pract. Exp., vol. 41, no. 1, pp. 23–50, 2011.
[15] P. Mach and Z. Becvar, “Mobile edge computing: A survey on architecture and computation offloading,” IEEE Commun. Surv. tutorials, vol. 19, no. 3, pp. 1628–1656, 2017.
[16] W. F. Vranken et al., “The CCPN data model for NMR spectroscopy: development of a software pipeline,” Proteins Struct. Funct. Bioinforma., vol. 59, no. 4, pp. 687–696, 2005.
[17] Q. Zhang, L. Cheng, and R. Boutaba, “Cloud computing: state-of-the-art and research challenges,” J. internet Serv. Appl., vol. 1, no. 1, pp. 7–18, 2010.
[18] R. Y. Zhong, X. Xu, E. Klotz, and S. T. Newman, “Intelligent manufacturing in the context of industry 4.0: a review,” Engineering, vol. 3, no. 5, pp. 616–630, 2017.
[19] S. Wolfert, L. Ge, C. Verdouw, and M.-J. Bogaardt, “Big data in smart farming–a review,” Agric. Syst., vol. 153, pp. 69–80, 2017.
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