Cloud-Based Architectural Framework for Scalable and High-Performance Smart Applications
Article Sidebar
Main Article Content
Abstract
The rapid evolution of smart applications, driven by advancements in the Internet of Things (IoT), artificial intelligence (AI), and big data analytics, has significantly increased the demand for scalable and high-performance computing infrastructures. Traditional architecture often struggles to meet these requirements due to limitations in resource scalability, processing efficiency, and system flexibility. The proposed framework emphasizes a layered architecture consisting of data acquisition, processing, service management, and application layers, ensuring efficient data flow and resource utilization. It leverages cloud-native principles to enable horizontal scalability, fault tolerance, and continuous deployment. Additionally, the integration of edge computing reduces latency by processing time-sensitive data closer to the source, thereby improving real-time responsiveness. Performance optimization techniques, including auto-scaling and load balancing, are incorporated to ensure consistent system performance under varying workloads. The framework also addresses critical challenges such as interoperability, security, and resource management by incorporating standardized interfaces and intelligent orchestration mechanisms. Experimental analysis and conceptual evaluation indicate that the proposed architecture significantly enhances system scalability, reduces latency, and improves overall application performance compared to traditional models. This study contributes to the field by providing a comprehensive architectural model that aligns with the evolving requirements of modern smart applications. The findings demonstrate that cloud-based frameworks, when combined with emerging technologies, can effectively support large-scale, high-performance systems. The proposed approach offers valuable insights for researchers and practitioners in designing next-generation smart application infrastructures.
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
[1] P. Mell and T. Grance, “The NIST definition of cloud computing,” 2011.
[2] R. Buyya, C. S. Yeo, S. Venugopal, J. Broberg, and I. Brandic, “Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility,” Futur. Gener. Comput. Syst., vol. 25, no. 6, pp. 599–616, 2009, doi: 10.1016/j.future.2008.12.001.
[3] M. I. Alam, M. A. Sami, M. A. K. P. Hemal, and M. L. Rahman, “Predictive Analytics and Decision Intelligence for Climate-Resilient Agritech Systems,” Acad. Glob. J. Comput. Sci. Technol. Stud., vol. 2, no. 1, pp. 44–56, 2023, doi: 10.32996/agjcsts.2023.2.1.4.
[4] 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.
[5] Q. Zhang, M. Chen, L. Li, and W. Zhao, “Machine learning in cloud computing: A survey,” IEEE Trans. Cloud Comput., 2018.
[6] M. Armbrust et al., “A view of cloud computing,” Commun. ACM, vol. 53, no. 4, pp. 50–58, 2010.
[7] C. Pahl, “Containerization and the paas cloud,” IEEE Cloud Comput., vol. 2, no. 3, pp. 24–31, 2015.
[8] T. Lorido-Botran, J. Miguel-Alonso, and J. A. Lozano, “A review of auto-scaling techniques for elastic applications in cloud environments,” J. Grid Comput., vol. 12, no. 4, pp. 559–592, 2014, doi: 10.1007/s10723-014-9314-7.
[9] M. Satyanarayanan, “The emergence of edge computing,” Computer (Long. Beach. Calif)., vol. 50, no. 1, pp. 30–39, 2017, doi: 10.1109/MC.2017.9.
[10] M. Ali, S. U. Khan, and A. V Vasilakos, “Security in cloud computing: Opportunities and challenges,” Inf. Sci. (Ny)., vol. 305, pp. 357–383, 2015.
[11] D. Merkel, “Docker: lightweight linux containers for consistent development and deployment,” Linux j, vol. 239, no. 2, p. 2, 2014.
[12] 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.
[13] I. Baldini et al., “Serverless computing: Current trends and open problems,” in Research advances in cloud computing, Springer, 2017, pp. 1–20.
[14] N. Vanu, M. R. Hasan, T. R. Sikder, and Z. S. Tamanna, “AI-Driven Big Data Analytics for Precision Medicine: A Unified Framework Integrating Molecular Data Intelligence, Wearable Health Systems, and Predictive Modeling,” J. Comput. Sci. Technol. Stud., vol. 3, no. 2, pp. 124–141, 2021.
[15] T. R. Sikder, S. Dash, B. Uddin, and F. Hossain, “AI-Powered Data Analytics and Multi-Omics Integration for Next-Generation Precision Oncology and Anticancer Drug Development,” Eastasouth J. Inf. Syst. Comput. Sci., vol. 1, no. 02 SE-Articles, pp. 153–170, Dec. 2023, doi: 10.58812/esiscs.v1i02.838.
[16] T. R. Sikder, M. A. Siam, M. M. H. Melon, S. M. M. Uddin, S. C. Mohonta, and F. Karim, “A Multimodal Data Analytics Framework for Early Cancer Detection Using Genomic, Radiomic, and Clinical Big Data Fusion,” J. Comput. Sci. Technol. Stud., vol. 5, no. 3, pp. 183–188, 2023.
[17] S. Marston, Z. Li, S. Bandyopadhyay, J. Zhang, and A. Ghalsasi, “Cloud computing—The business perspective,” Decis. Support Syst., vol. 51, no. 1, pp. 176–189, 2011, doi: 10.1016/j.dss.2010.12.006.
[18] L. Atzori, A. Iera, and G. Morabito, “The internet of things: A survey,” Comput. networks, vol. 54, no. 15, pp. 2787–2805, 2010.
[19] M. Chen, S. Mao, and Y. Liu, “Big data: A survey,” Mob. Networks Appl., vol. 19, no. 2, pp. 171–209, 2014, doi: 10.1007/s11036-013-0489-0.
[20] K. Hashizume, D. G. Rosado, E. Fernández-Medina, and E. B. Fernandez, “An analysis of security issues for cloud computing,” J. Internet Serv. Appl., vol. 4, no. 1, pp. 1–13, 2013, doi: 10.1186/1869-0238-4-5.
[21] S. C. Barman, S. Raval, and M. A. Hossian, “Socioeconomic and institutional determinants of public acceptance of waste-to-energy policies: Evidence for sustainable energy transitions,” Innov. Int. Multidiscip. J. Appl. Technol., vol. 1, no. 2, pp. 65–75, 2023, doi: 10.51699/rhs7k850.
[22] S. C. Barman, Z. Wang, G. Yasin, and M. F. Wen, “Experimental validation of earth abundant heterogeneous catalysts toward sustainable energy conversion,” Cent. Asian J. Theor. Appl. Sci., vol. 3, no. 3, pp. 93–102, 2022, doi: 10.51699/cajotas.v3i3.1662.
[23] S. C. Barman and A. I. Opy, “Integrated artificial intelligence and stochastic optimization framework for resilient and low carbon renewable energy manufacturing systems,” Energy Environ. Econ., vol. 1, no. 1, pp. 1–8, 2023, doi: 10.25163/energy.1110684.
[24] S. Islam, E. Hossain, M. S. Rahman, M. M. Rahman, S. I. Khan, and A. A. M. Ashik, “Digital transformation in SMEs: Unlocking competitive advantage through business intelligence and data analytics adoption,” J. Bus. Manag. Stud., vol. 5, no. 6, pp. 177–186, 2023, doi: 10.32996/jbms.2023.5.6.14.
[25] A. A. M. Ashik, M. M. Rahman, E. Hossain, M. S. Rahman, S. Islam, and S. I. Khan, “Transforming U.S. healthcare profitability through data-driven decision making: Applications, challenges, and future directions,” Eur. J. Med. Health Res., vol. 1, no. 3, pp. 116–125, 2023, doi: 10.59324/ejmhr.2023.1(3).21.
[26] E. Hossain, A. A. M. Ashik, M. M. Rahman, S. I. Khan, M. S. Rahman, and S. Islam, “Big data and migration forecasting: Predictive insights into displacement patterns triggered by climate change and armed conflict,” J. Comput. Sci. Technol. Stud., vol. 5, no. 4, pp. 265–274, 2023, doi: 10.32996/jcsts.2023.5.4.27.