Next-Generation Cloud Computing Architecture for Secure, Sustainable, and Intelligent Digital Services
Keywords:
Cloud Computing, Artificial Intelligence, Green Computing, Cybersecurity, Edge Computing, Sustainability, Digital Services.Abstract
Cloud computing has evolved from basic virtualization and distributed computing paradigms into a fundamental infrastructure supporting modern digital services across industries. The rapid growth of cloud-based applications, Internet of Things (IoT) devices, big data analytics, and artificial intelligence (AI) has significantly increased demands for scalability, security, sustainability, and intelligent resource management. However, existing cloud architectures often face challenges related to cybersecurity threats, excessive energy consumption, resource inefficiencies, and limited adaptability to dynamic workloads. To address these issues, this study proposes a Next-Generation Cloud Computing Architecture designed to deliver secure, sustainable, and intelligent digital services. The proposed framework integrates an AI-driven resource management layer, a zero-trust security model, intelligent threat detection mechanisms, energy-aware workload scheduling, and edge-cloud collaboration to enhance overall system performance. The research methodology combines architectural design, mathematical modeling, simulation-based evaluation, and comparative performance analysis using key metrics such as resource utilization, energy efficiency, security effectiveness, and response time. Experimental results demonstrate that the proposed architecture achieves improved resource allocation accuracy, enhanced threat detection capability, reduced energy consumption, and faster service delivery compared to conventional cloud environments. Furthermore, the integration of sustainability-oriented strategies contributes to lower operational costs and reduced carbon emissions while maintaining high service quality. The findings highlight the potential of combining AI, cybersecurity, and green computing principles within a unified cloud ecosystem. Future developments may further extend the architecture through quantum-resistant security, autonomous cloud management, and advanced edge intelligence, enabling resilient and sustainable digital infrastructures for next-generation applications.
References
1. Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., & Zaharia, M. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50–58. https://doi.org/10.1145/1721654.1721672
2. Buyya, R., Yeo, C. S., Venugopal, S., Broberg, J., & Brandic, I. (2009). Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the fifth utility. Future Generation Computer Systems, 25(6), 599–616. https://doi.org/10.1016/j.future.2008.12.001
3. Mell, P., & Grance, T. (2011). The NIST definition of cloud computing. National Institute of Standards and Technology. https://doi.org/10.6028/NIST.SP.800-145
4. Stergiou, C., Psannis, K. E., Kim, B. G., & Gupta, B. (2018). Secure integration of IoT and cloud computing. Future Generation Computer Systems, 78, 964–975. https://doi.org/10.1016/j.future.2016.11.031
5. Khan, M. A., & Salah, K. (2018). IoT security: Review, blockchain solutions, and open challenges. Future Generation Computer Systems, 82, 395–411. https://doi.org/10.1016/j.future.2017.11.022
6. Zhang, Y., Chen, X., Li, J., Wong, D. S., & Xiang, Y. (2019). Ensuring attribute privacy protection and fast decryption for outsourced data security in mobile cloud computing. Information Sciences, 379, 42–61. https://doi.org/10.1016/j.ins.2016.01.074
7. Aloufi, K., & Haddadi, H. (2020). Energy-efficient cloud computing: Techniques and challenges. Sustainable Computing: Informatics and Systems, 28, 100417. https://doi.org/10.1016/j.suscom.2020.100417
8. Beloglazov, A., Abawajy, J., & Buyya, R. (2012). Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Generation Computer Systems, 28(5), 755–768. https://doi.org/10.1016/j.future.2011.04.017
9. Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016). Edge computing: Vision and challenges. IEEE Internet of Things Journal, 3(5), 637–646. https://doi.org/10.1109/JIOT.2016.2579198
10. Satyanarayanan, M. (2017). The emergence of edge computing. Computer, 50(1), 30–39. https://doi.org/10.1109/MC.2017.9
11. Yu, W., Liang, F., He, X., Hatcher, W. G., Lu, C., Lin, J., & Yang, X. (2018). A survey on the edge computing for the Internet of Things. IEEE Access, 6, 6900–6919. https://doi.org/10.1109/ACCESS.2017.2778504
12. Chen, M., Challita, U., Saad, W., Yin, C., & Debbah, M. (2019). Artificial neural networks-based machine learning for wireless networks. IEEE Communications Surveys & Tutorials, 21(4), 3039–3071. https://doi.org/10.1109/COMST.2019.2926625
13. Rjoub, G., Aljamaan, H., Alruwaili, M., Alshammari, M., & Alrashidi, M. (2023). Artificial intelligence-enabled sustainable cloud computing: A comprehensive review. Sustainable Computing: Informatics and Systems, 39, 100904. https://doi.org/10.1016/j.suscom.2023.100904
14. Gajula, S. (2023). A Review of Anomaly Identification in Finance Frauds using Machine Learning System. International Journal of Current Engineering and Technology, 13(06).
15. Gajula, S. (2024). Cybersecurity risk prediction using graph neural networks. Journal of Information Systems Engineering and Management.
16. Rahman¹, M. A., Haque, B. T., Hossan, M. I., & Rubel⁴, M. S. K. C. (2024). Federated Threat Intelligence and Explainable Anomaly Detection for Distributed Network Environments in US Critical Infrastructure.
17. Rahman, M. A., & Haque, B. T. (2023). Advancing Explainable and Secure Machine Learning for Decision Support in US Regulated Systems. ASRC Procedia: Global Perspectives in Science and Scholarship, 3(1), 231-273.
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