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A Visualization Review of Cloud Computing Algorithms in the Last Decade

Author

Listed:
  • Junhu Ruan

    (College of Economics and Management, Northwest A & F University, Yangling 712100, China
    Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China
    Faculty of Management and Economics, Dalian University of Technology, Dalian 116023, China)

  • Felix T. S. Chan

    (Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China)

  • Fangwei Zhu

    (Faculty of Management and Economics, Dalian University of Technology, Dalian 116023, China)

  • Xuping Wang

    (Faculty of Management and Economics, Dalian University of Technology, Dalian 116023, China)

  • Jing Yang

    (Department of Electronic Information Engineering, Handan Polytechnic College, Handan 056001, China)

Abstract

Cloud computing has competitive advantages—such as on-demand self-service, rapid computing, cost reduction, and almost unlimited storage—that have attracted extensive attention from both academia and industry in recent years. Some review works have been reported to summarize extant studies related to cloud computing, but few analyze these studies based on the citations. Co-citation analysis can provide scholars a strong support to identify the intellectual bases and leading edges of a specific field. In addition, advanced algorithms, which can directly affect the availability, efficiency, and security of cloud computing, are the key to conducting computing across various clouds. Motivated by these observations, we conduct a specific visualization review of the studies related to cloud computing algorithms using one mainstream co-citation analysis tool—CiteSpace. The visualization results detect the most influential studies, journals, countries, institutions, and authors on cloud computing algorithms and reveal the intellectual bases and focuses of cloud computing algorithms in the literature, providing guidance for interested researchers to make further studies on cloud computing algorithms.

Suggested Citation

  • Junhu Ruan & Felix T. S. Chan & Fangwei Zhu & Xuping Wang & Jing Yang, 2016. "A Visualization Review of Cloud Computing Algorithms in the Last Decade," Sustainability, MDPI, vol. 8(10), pages 1-16, October.
    • Handle: RePEc:gam:jsusta:v:8:y:2016:i:10:p:1008-:d:80200
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    References listed on IDEAS

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    6. Yen-Chieh Tseng & DaSheng Lee & Cheng-Fang Lin & Ching-Yuan Chang, 2016. "The Energy Savings and Environmental Benefits for Small and Medium Enterprises by Cloud Energy Management System," Sustainability, MDPI, vol. 8(6), pages 1-13, June.
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    Cited by:

    1. Wenwen Zhu & Zhiqiang Wang, 2018. "The Collaborative Networks and Thematic Trends of Research on Purchasing and Supply Management for Environmental Sustainability: A Bibliometric Review," Sustainability, MDPI, vol. 10(5), pages 1-28, May.
    2. Rong Xie & Muyan Chen & Weihuang Liu & Hongfei Jian & Yanjun Shi, 2021. "Digital Twin Technologies for Turbomachinery in a Life Cycle Perspective: A Review," Sustainability, MDPI, vol. 13(5), pages 1-22, February.
    3. Wei Song & Zhiya Chen & Xuping Wang & Qian Wang & Chenghua Shi & Wei Zhao, 2017. "Environmentally Friendly Supplier Selection Using Prospect Theory," Sustainability, MDPI, vol. 9(3), pages 1-17, March.
    4. Jiaxi Luo, 2022. "A Bibliometric Review on Artificial Intelligence for Smart Buildings," Sustainability, MDPI, vol. 14(16), pages 1-22, August.
    5. Francisco-Javier Ferrández-Pastor & Higinio Mora & Antonio Jimeno-Morenilla & Bruno Volckaert, 2018. "Deployment of IoT Edge and Fog Computing Technologies to Develop Smart Building Services," Sustainability, MDPI, vol. 10(11), pages 1-23, October.

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