International Journal of Information Science and Management (IJISM)

Current Issue

By Issue

By Author

By Subject

Author Index

Keyword Index

About Journal

Aims and Scope

Editorial Board

Publication Ethics

Indexing and Abstracting

Peer Review Process

Journal Metrics

Open Access Policy

Language editing services

Identifying Influential Trends in the Cloud Computing Pricing Research Using Computational Literature Review and Social Network Analysis Centrality Measures

Document Type : Articles

Authors

1 Ph.D. Candidate, Department of Business Management, Faculty of Management, University of Tehran, Tehran, Iran

2 Associate Professor, Department of Business Management, Faculty of Management, University of Tehran, Tehran, Iran

3 Professor, Department of Decision and Information Science, Charlton College of Business, University of Massachusetts, Dartmouth, United States

Abstract

Cloud computing has become one of the newest and most popular topics in the field of the Internet. Pricing is one of the main factors that can affect the successful implementation of cloud computing. Due to the large volume of research conducted in this field, the purpose of this study is to review the cloud computing pricing literature using a Computational Literature Review (CLR) and identify influential trends in this field. For this purpose, the publication and citation trends are first identified. The most influential authors, journals, and articles are then determined using citation analysis. Next, the structure of the co-occurrence network of keywords is analyzed using three centrality measures degree, betweenness, and closeness. Finally, the thematic trends are identified using a positional analysis based on centrality measures. According to the obtained results, research in this field has grown significantly. Keywords such as edge, computer architecture, and distributed computing have recently come to the fore. Also, words such as model, energy, allocation, strategy, auction, design, and reliability have been among the most influential in this field. The positional analysis indicates that the researchers are trying to overcome resource scarcity through three lines of work: resource provision, resource allocation, and resource distribution. Trends show that the cloud industry is highly attractive and will also have high growth. In the future, we will also see an increase in the use of value-based pricing methods in cloud computing and research in this area.https://dorl.net/dor/20.1001.1.20088302.2022.20.4.12.1

Keywords

References
  1. Abadi, D. J. (2009). Data management in the cloud: Limitations and opportunities. IEEE Data Eng. Bull., 32(1), 3–12.
  2. Abrishami, S., Naghibzadeh, M., & Epema, D. H. J. (2013). Deadline-constrained workflow scheduling algorithms for infrastructure as a service clouds. Future Generation Computer Systems, 29(1), 158–169.
  3. Al-Roomi, M., Al-Ebrahim, S., Buqrais, S., & Ahmad, I. (2013). Cloud computing pricing models: a survey. International Journal of Grid and Distributed Computing, 6(5), 93–106.
  4. Alkharif, S., Lee, K., & Kim, H. (2018). Time-series analysis for price prediction of opportunistic cloud computing resources. Proceedings of the 7th International Conference on Emerging Databases, 221–229. Springer.
  5. Anuradha, V. P., & Sumathi, D. (2014). A survey on resource allocation strategies in cloud computing. International Conference on Information Communication and Embedded Systems (ICICES2014), 1–7. IEEE.
  6. Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., … Stoica, I. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50–58.
  7. Arunarani, A. R., Manjula, D., & Sugumaran, V. (2019). Task scheduling techniques in cloud computing: A literature survey. Future Generation Computer Systems, 91, 407–415.
  8. Banijamali, A., Pakanen, O.-P., Kuvaja, P., & Oivo, M. (2020). Software architectures of the convergence of cloud computing and the Internet of Things: A systematic literature review. Information and Software Technology, 122, 106271.
  9. Bastian, M., Heymann, S., & Jacomy, M. (2009). Gephi: an open source software for exploring and manipulating networks. Proceedings of the International AAAI Conference on Web and Social Media, 3(1).
  10. Bera, S., Misra, S., & Rodrigues, J. J. P. C. (2014). Cloud computing applications for smart grid: A survey. IEEE Transactions on Parallel and Distributed Systems, 26(5), 1477–1494.
  11. Bhardwaj, S., Jain, L., & Jain, S. (2010). Cloud computing: A study of infrastructure as a service (IAAS). International Journal of Engineering and Information Technology, 2(1), 60–63.
  12. Bornmann, L., & Daniel, H. (2008). What do citation counts measure? A review of studies on citing behavior. Journal of Documentation.
  13. Bustinza, O. F., Bigdeli, A. Z., Baines, T., & Elliot, C. (2015). Servitization and competitive advantage: the importance of organizational structure and value chain position. Research-Technology Management, 58(5), 53–60.
  14. Chaisiri, S., Lee, B.-S., & Niyato, D. (2011). Optimization of resource provisioning cost in cloud computing. IEEE Transactions on Services Computing, 5(2), 164–177.
  15. Chauhan, S. S., Pilli, E. S., Joshi, R. C., Singh, G., & Govil, M. C. (2019). Brokering in interconnected cloud computing environments: A survey. Journal of Parallel and Distributed Computing, 133, 193–209.
  16. Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 1165–1188.
  17. Chen, J., Li, K., Rong, H., Bilal, K., Li, K., & Philip, S. Y. (2019). A periodicity-based parallel time series prediction algorithm in cloud computing environments. Information Sciences, 496, 506–537.
  18. Chesbrough, H. W. (2011). Bringing open innovation to services. MIT Sloan Management Review, 52(2), 85.
  19. Dotsika, F., & Watkins, A. (2017). Identifying potentially disruptive trends by means of keyword network analysis. Technological Forecasting and Social Change, 119, 114–127.
  20. Dutta, S., Zbaracki, M. J., & Bergen, M. (2003). Pricing process as a capability: A resource‐based perspective. Strategic Management Journal, 24(7), 615–630.
  21. Fox, A., Griffith, R., Joseph, A., Katz, R., Konwinski, A., Lee, G., … Stoica, I. (2009). Above the clouds: A berkeley view of cloud computing. Dept. Electrical Eng. and Comput. Sciences, University of California, Berkeley, Rep. UCB/EECS, 28(13), 2009.
  22. Grand View Research Inc. (2021). Cloud Computing Market Size, Share & Trends Analysis Report By Service (SaaS, IaaS), By Enterprise Size (Large Enterprises, SMEs), By End Use (BFSI, Manufacturing), By Deployment, And Segment Forecasts, 2021 - 2028. Retrieved from https://www.grandviewresearch.com/industry-analysis/cloud-computing-industry?utm_source=prnewswire&utm_medium=referral&utm_campaign=ict_01-sep-21&utm_term=cloud-computing-market&utm_content=rd1
  23. Greisman, j. (2007). pricing: the thankless job that somenone has to do. Retrieved from http://www.softwarepricing.com/
  24. Guerrero-Ibanez, J. A., Zeadally, S., & Contreras-Castillo, J. (2015). Integration challenges of intelligent transportation systems with connected vehicle, cloud computing, and internet of things technologies. IEEE Wireless Communications, 22(6), 122–128.
  25. Hammersley, M. (2001). On’systematic’reviews of research literatures: a’narrative’response to Evans & Benefield. British Educational Research Journal, 27(5), 543–554.
  26. Hosseini, M. R., Martek, I., Zavadskas, E. K., Aibinu, A. A., Arashpour, M., & Chileshe, N. (2018). Critical evaluation of off-site construction research: A Scientometric analysis. Automation in Construction, 87, 235–247.
  27. Hsu, P.-F., Ray, S., & Li-Hsieh, Y.-Y. (2014). Examining cloud computing adoption intention, pricing mechanism, and deployment model. International Journal of Information Management, 34(4), 474–488.
  28. Huang, C., Min, G., Wu, Y., Ying, Y., Pei, K., & Xiang, Z. (2017). Time series anomaly detection for trustworthy services in cloud computing systems. IEEE Transactions on Big Data.
  29. Huang, K.-C., & Shen, B.-J. (2015). Service deployment strategies for efficient execution of composite SaaS applications on cloud platform. Journal of Systems and Software, 107, 127–141.
  30. Hussain, M., & Abdulsalam, H. M. (2014). Software quality in the clouds: a cloud-based solution. Cluster Computing, 17(2), 389–402.
  31. Jennings, B., & Stadler, R. (2015). Resource management in clouds: Survey and research challenges. Journal of Network and Systems Management, 23(3), 567–619.
  32. Khan, A., Yan, X., Tao, S., & Anerousis, N. (2012). Workload characterization and prediction in the cloud: A multiple time series approach. 2012 IEEE Network Operations and Management Symposium, 1287–1294. IEEE.
  33. Kumar, D., Baranwal, G., Raza, Z., & Vidyarthi, D. P. (2018). A survey on spot pricing in cloud computing. Journal of Network and Systems Management, 26(4), 809–856.
  34. Liang, Y., & Chen, J. (2011). Group network centrality analysis of blogs in politics. Communications in Information Science and Management Engineering, 1(3).
  35. Liaqat, M., Chang, V., Gani, A., Ab Hamid, S. H., Toseef, M., Shoaib, U., & Ali, R. L. (2017). Federated cloud resource management: Review and discussion. Journal of Network and Computer Applications, 77, 87–105.
  36. Lin, J., Keogh, E., Lonardi, S., & Chiu, B. (2003). A symbolic representation of time series, with implications for streaming algorithms. Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, 2–11.
  37. Liu, Y., & Xu, X. (2017). Industry 4.0 and cloud manufacturing: A comparative analysis. Journal of Manufacturing Science and Engineering, 139(3).
  38. Lyons, K., Playford, C., Messinger, P. R., Niu, R. H., & Stroulia, E. (2009). Business models in emerging online services. SIGeBIZ Track of the Americas Conference on Information Systems, 44–55. Springer.
  39. Markoulli, M. P., Lee, C. I. S. G., Byington, E., & Felps, W. A. (2017). Mapping Human Resource Management: Reviewing the field and charting future directions. Human Resource Management Review, 27(3), 367–396.
  40. Mazrekaj, A., Shabani, I., & Sejdiu, B. (2016). Pricing schemes in cloud computing: an overview. International Journal of Advanced Computer Science and Applications, 7(2), 80–86.
  41. Mell, P., & Grance, T. (2011). The NIST Definition of Cloud Computing-Recommendations of the National Institute of Standards and Technology. NIST. NIST Special Publication, 145–800.
  42. Mortenson, M. J., & Vidgen, R. (2016). A computational literature review of the technology acceptance model. International Journal of Information Management, 36(6), 1248–1259.
  43. Mrozek, D. (2020). A review of Cloud computing technologies for comprehensive microRNA analyses. Computational Biology and Chemistry, 107365.
  44. Ojala, A. (2016). Adjusting software revenue and pricing strategies in the era of cloud computing. Journal of Systems and Software, 122, 40–51.
  45. Olawumi, T. O., & Chan, D. W. M. (2018). A scientometric review of global research on sustainability and sustainable development. Journal of Cleaner Production, 183, 231–250.
  46. Oxford Economics and SAP. (2014). The cloud grows up. Retrieved from https://www.oxfordeconomics.com/recentreleases/%0Athe-cloud-grows-up
  47. Pawluk, P., Simmons, B., Smit, M., Litoiu, M., & Mankovski, S. (2012). Introducing STRATOS: A cloud broker service. 2012 IEEE Fifth International Conference on Cloud Computing, 891–898. IEEE.
  48. Radhakrishnan, S., Erbis, S., Isaacs, J. A., & Kamarthi, S. (2017). Novel keyword co-occurrence network-based methods to foster systematic reviews of scientific literature. PloS One, 12(3), e0172778.
  49. RightScale. (2016). Cloud Computing Trends: 2016 State of the Cloud Survey.
  50. Sahal, R., Khafagy, M. H., & Omara, F. A. (2016). A survey on SLA management for cloud computing and cloud-hosted big data analytic applications. International Journal of Database Theory and Application, 9(4), 107–118.
  51. Saltan, A., & Smolander, K. (2021). Bridging the state-of-the-art and the state-of-the-practice of SaaS pricing: A multivocal literature review. Information and Software Technology, 106510.
  52. Shi, W., Zhang, L., Wu, C., Li, Z., & Lau, F. C. M. (2014). An online auction framework for dynamic resource provisioning in cloud computing. ACM SIGMETRICS Performance Evaluation Review, 42(1), 71–83.
  53. Singh, A., & Chatterjee, K. (2017). Cloud security issues and challenges: A survey. Journal of Network and Computer Applications, 79, 88–115.
  54. Singhal, R., & Singhal, A. (2021). A feedback-based combinatorial fair economical double auction resource allocation model for cloud computing. Future Generation Computer Systems, 115, 780–797.
  55. Su, H.-N., & Lee, P.-C. (2010). Mapping knowledge structure by keyword co-occurrence: a first look at journal papers in Technology Foresight. Scientometrics, 85(1), 65–79.
  56. Sultan, N. (2014a). Making use of cloud computing for healthcare provision: Opportunities and challenges. International Journal of Information Management, 34(2), 177–184.
  57. Sultan, N. (2014b). Servitization of the IT industry: the cloud phenomenon. Strategic Change, 23(5–6), 375–388.
  58. Sun, H., Tu, Q. W., Wang, X. W., Zhang, J. H., Wu, Q. Z., & Qin, S. W. (2013). The Pricing and Charging of Cloud Computing SaaS. Advanced Materials Research, 798, 703–707. Trans Tech Publ.
  59. Sun, X., Zhuo, X., & Wang, Z. (2020). A Survey of Pricing Aware Traffic Engineering in Cloud Computing. Journal of Internet Technology, 21(2), 357–364.
  60. Tian, F., Qin, T., & Liu, T.-Y. (2018). Computational pricing in Internet era. Frontiers of Computer Science, 12(1), 40–54.
  61. Tordsson, J., Montero, R. S., Moreno-Vozmediano, R., & Llorente, I. M. (2012). Cloud brokering mechanisms for optimized placement of virtual machines across multiple providers. Future Generation Computer Systems, 28(2), 358–367.
  62. Tsai, W., Bai, X., & Huang, Y. (2014). Software-as-a-service (SaaS): perspectives and challenges. Science China Information Sciences, 57(5), 1–15.
  63. van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3
  64. Van Raan, A. F. J., Visser, M. S., Van Leeuwen, T. N., & Van Wijk, E. (2003). Bibliometric analysis of psychotherapy research: Performance assessment and position in the journal landscape. Psychotherapy Research, 13(4), 511–528.
  65. Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications (Vol. 8). Cambridge university press.
  66. Wei, M. (2019). Empirical Study on the Benefit Distribution Model of Port Supply Chain under Cloud Environment. Journal of Coastal Research, 94(SI), 793–797.
  67. Wuni, I. Y., Shen, G. Q. P., & Osei-Kyei, R. (2019). Scientometric review of global research trends on green buildings in construction journals from 1992 to 2018. Energy and Buildings.
  68. Zhu, Z., Zhang, G., Li, M., & Liu, X. (2015). Evolutionary multi-objective workflow scheduling in cloud. IEEE Transactions on Parallel and Distributed Systems, 27(5), 1344–1357.
  69. Zurian, J. C. V., Cañigral, F. J. B., Cogollos, L. C., & Aleixandre-Benavent, R. (2021). The most 100 cited papers in addiction research on cannabis, heroin, cocaine and psychostimulants. A bibliometric cross-sectional analysis. Drug and Alcohol Dependence, 221, 108616.
International Journal of Information Science and Management (IJISM)
Volume 20, Issue 4 - Serial Number 4
October 2022
Pages 193-218
Files
History
  • Receive Date: 27 November 2021
  • Revise Date: 20 December 2022
  • Accept Date: 03 December 2022
Share
How to cite
Statistics