Agentic AI Control Plane for Sovereign, Cloud-Native Payment Authorizations
Keywords:
agentic AI, cloud-native, payment authorization, sovereignty, reinforcement learningAbstract
Geopolitical domains present a complex orchestration challenge for evolving cloud-native payment infrastructures, maintaining regulatory compliance, sovereignty, and ultra-low latency. The objective of this paper is to introduce an agentic AI control panel, which is especially designed for sovereign, cloud-native payment authorization systems.
Downloads
References
S. K. Sharma, A. Dwivedi, and S. Singh, "Cloud-native architectures for payment systems: challenges and opportunities," IEEE Cloud Computing, vol. 8, no. 1, pp. 30–38, Jan.–Mar. 2021.
J. Xu, W. Zhao, and Y. Liu, "Data sovereignty in cloud computing: a survey of regulatory compliance and technical challenges," IEEE Trans. Cloud Comput., vol. 9, no. 2, pp. 412–427, Apr.–Jun. 2021.
F. Li, J. Luo, and P. Liu, "Reinforcement learning-based resource orchestration for cloud-native applications," IEEE Trans. Services Computing, vol. 14, no. 4, pp. 1349–1362, Jul.–Aug. 2021.
M. Al-Muhaisen, R. Abdi, and H. Alshatri, "Multi-region failover strategies in cloud computing: a systematic review," IEEE Access, vol. 9, pp. 12456–12472, 2021.
A. Gupta and N. Sharma, "Autonomous orchestration of cloud workloads using deep reinforcement learning," in Proc. IEEE Int. Conf. Cloud Eng. (IC2E), 2021, pp. 142–151.
P. Mishra and A. Jain, "Regulatory boundary detection in distributed cloud systems," IEEE Trans. Dependable Secure Comput., vol. 18, no. 3, pp. 1185–1197, May–Jun. 2021.
C. Tang and K. Chen, "Predictive analytics for regulatory compliance in financial cloud systems," IEEE Trans. Big Data, vol. 7, no. 1, pp. 47–60, Mar. 2021.
J. Kim, S. Lee, and Y. Kim, "AI-driven compliance management framework for sovereign cloud services," in Proc. IEEE Int. Conf. Cloud Comput. Technol. Sci. (CloudCom), 2020, pp. 234–241.
H. Zhang, Z. Liu, and M. Chen, "Cloud-native payment processing: architecture and performance," IEEE Trans. Parallel Distrib. Syst., vol. 32, no. 4, pp. 976–987, Apr. 2021.
L. Wang and M. Guo, "Load prediction and dynamic resource allocation in multi-region cloud platforms," IEEE Trans. Network Service Manag., vol. 18, no. 2, pp. 1235–1247, Jun. 2021.
T. Nguyen and B. Park, "Multi-agent systems for cloud orchestration: reinforcement learning approaches," IEEE Trans. Systems Man Cybernetics: Systems, vol. 51, no. 7, pp. 4280–4292, Jul. 2021.
D. Singh and R. Buyya, "Fault tolerance and failover in cloud-native systems: a survey," IEEE Commun. Surveys Tuts., vol. 23, no. 1, pp. 550–579, Firstquarter 2021.
K. Lee, S. Kim, and Y. Park, "Compliance-aware orchestration of cloud-native applications for financial services," in Proc. IEEE Int. Conf. Financial Cryptogr. Data Security (FC), 2020, pp. 178–193.
M. Zhang and S. Jha, "Secure and efficient replica placement in sovereign cloud infrastructures," IEEE Trans. Cloud Comput., vol. 9, no. 3, pp. 1210–1223, Jul.–Sep. 2021.
J. Li, X. Wang, and H. Guan, "Network path optimization for low latency cloud payments," IEEE/ACM Trans. Netw., vol. 29, no. 3, pp. 1280–1293, Jun. 2021.
Y. Chen, H. Xu, and S. Gao, "Autonomous failover management using AI in distributed cloud payment systems," IEEE Trans. Services Computing, vol. 14, no. 1, pp. 175–187, Jan.–Feb. 2021.
R. Kumar and P. Sharma, "Towards autonomous cloud orchestration: a reinforcement learning perspective," IEEE Trans. Neural Netw. Learn. Syst., vol. 32, no. 6, pp. 2394–2407, Jun. 2021.
V. Singh and A. K. Sahu, "Data sovereignty and encryption compliance in cloud payments," in Proc. IEEE Int. Conf. Data Eng. (ICDE), 2021, pp. 1898–1907.
B. Zhao and X. Zhang, "Policy-driven resource orchestration for sovereign cloud services," IEEE Trans. Services Computing, vol. 14, no. 3, pp. 840–852, May–Jun. 2021.
N. Patel, A. Desai, and M. Shah, "Predictive regulatory compliance in cloud financial platforms using machine learning," IEEE Access, vol. 9, pp. 57433–57444, 2021.