Ethical Real-Time AI and Cloud Framework for Software-Defined Networks: Database-Integrated Automation of Business Logic in Adaptive Environments
DOI:
https://doi.org/10.15680/IJCTECE.2022.0506012Keywords:
ethical AI, real-time automation, cloud computing, software-defined networks (SDN), business rules automation, governance, transparency, accountability, dynamic networks, business-rule engineAbstract
In dynamic cloud-native network environments, the convergence of artificial intelligence (AI), real-time automation, business-rule processing and software-defined networking (SDN) presents transformative opportunities but also profound ethical and governance challenges. This paper proposes a framework for real-time Ethical AI in cloud-based SDN infrastructures, enabling the intelligent automation of business rules while embedding transparency, accountability, fairness and privacy into network operations. The framework includes an AI decision-engine that monitors network state and business-rule triggers, an SDN control layer for enforcing flows and policies in real time, a cloud orchestration layer for scalable resource and rule management, and an ethics/governance module that audits decisions, logs rationale, enforces business-rule fairness and ensures human oversight. We describe the architecture, the design of business-rule automation workflows, the ethical governance mechanisms and the real-time control loops. We then present a prototype in a simulated cloud-SDN environment, showing that our framework can enforce business rules (e.g., service-level-agreements, priority flows for business clients) with low latency, adapt to changing conditions, and maintain audit logs and explainable decisions. Advantages include rapid business-rule deployment, improved agility, intelligent adaptation and embedded ethics; disadvantages include added system overhead, complexity of rule-AI integration and the challenge of securing real-time decision pipelines. The results and discussion explore the trade-offs between automation speed, ethical oversight, resource cost and rule complexity. We conclude that embedding ethical AI into real-time cloud-SDN business-rule automation is feasible and beneficial, but requires careful balancing. Future work includes deployment in multi-tenant environments, richer business-rule languages, integration of continuous ethics assessment and dynamic rule conflict resolution.
References
1. Ahmed, K., Blech, J. O., Gregory, M. A., & Schmidt, H. W. (2018). Software Defined Networks in Industrial Automation. Journal of Sensor and Actuator Networks, 7(3), 33. https://doi.org/10.3390/jsan7030033
2. Anand, L., Nallarasan, V., Krishnan, M. M., & Jeeva, S. (2020, October). Driver profiling-based anti-theft system. In AIP Conference Proceedings (Vol. 2282, No. 1, p. 020042). AIP Publishing LLC.
3. Konda, S. K. (2022). ENGINEERING RESILIENT INFRASTRUCTURE FOR BUILDING MANAGEMENT SYSTEMS: NETWORK RE-ARCHITECTURE AND DATABASE UPGRADE AT NESTLÉ PHX. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(1), 6186-6201.
4. Dong Wang, Lihua Dai (2022). Vibration signal diagnosis and conditional health monitoring of motor used in biomedical applications using Internet of Things environment. Journal of Engineering 5 (6):1-9.
5. Cherukuri, B. R. (2019). Future of cloud computing: Innovations in multi-cloud and hybrid architectures.
6. Kumbum, P. K., Adari, V. K., Chunduru, V. K., Gonepally, S., & Amuda, K. K. (2020). Artificial intelligence using TOPSIS method. Journal of Computer Science Applications and Information Technology, 5(1), 1–7. https://doi.org/10.15226/2474-9257/5/1/00147
7. Ali Khan, A., Badshah, S., Liang, P., Khan, B., Waseem, M., & Niazi, M. (2021). Ethics of AI: A Systematic Literature Review of Principles and Challenges. arXiv preprint arXiv:2109.07906.
8. Chowdhary, A., Alshamrani, A., & Huang, D. (2018). SUPC: SDN enabled Universal Policy Checking in Cloud Network. arXiv preprint arXiv:1811.00657.
9. Cherukuri, B. R. (2020). Ethical AI in Cloud: Mitigating risks in machine learning models. World Journal of Advanced Engineering Technology and Sciences, 01(01), 096-109.
10. Hemamalini, V., Anand, L., Nachiyappan, S., Geeitha, S., Motupalli, V. R., Kumar, R., ... & Rajesh, M. (2022). Integrating bio medical sensors in detecting hidden signatures of COVID-19 with Artificial intelligence. Measurement, 194, 111054.
11. Jammal, M., Singh, T., Shami, A., Asal, R., & Li, Y. (2014). Software-Defined Networking: State of the Art and Research Challenges. arXiv preprint arXiv:1406.0124.
12. Sugumar, R. (2016). An effective encryption algorithm for multi-keyword-based top-K retrieval on cloud data. Indian Journal of Science and Technology 9 (48):1-5.
13. Vengathattil, S. (2019). Ethical Artificial Intelligence - Does it exist? International Journal for Multidisciplinary Research, 1(3). https://doi.org/10.36948/ijfmr.2019.v01i03.37443
14. Anand, L., Rane, K. P., Bewoor, L. A., Bangare, J. L., Surve, J., Raghunath, M. P., ... & Osei, B. (2022). Development of machine learning and medical enabled multimodal for segmentation and classification of brain tumor using MRI images. Computational intelligence and neuroscience, 2022(1), 7797094.
15. Kwizera, P. (2021). Challenges of Cloud Computing: Selected Qualitative Narrative Review and Ethical Analysis Studies. HBCU Journal of Research Initiatives, 1(1).
16. Sharma, N. (2017). Assessing the Role of Software-Defined Networking (SDN) in Modern Cloud-Based Computing. International Journal of Intelligent Systems and Applications in Engineering.
17. Gosangi, S. R. (2022). SECURITY BY DESIGN: BUILDING A COMPLIANCE-READY ORACLE EBS IDENTITY ECOSYSTEM WITH FEDERATED ACCESS AND ROLE-BASED CONTROLS. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(3), 6802-6807.
18. AZMI, S. K. (2021). Markov Decision Processes with Formal Verification: Mathematical Guarantees for Safe Reinforcement Learning.
19. Tahirkheli, A. I., Shiraz, M., Hayat, B., Idrees, M., Sajid, A., Ullah, R., Ayub, N., & Kim, K.-I. (2021). A Survey on Modern Cloud Computing Security over Smart City Networks: Threats, Vulnerabilities, Consequences, Countermeasures, and Challenges. Electronics, 10(15), 1811. https://doi.org/10.3390/electronics10151811
20. Sugumar, R. (2022). Estimation of Social Distance for COVID19 Prevention using K-Nearest Neighbor Algorithm through deep learning. IEEE 2 (2):1-6.
21. Vinay Kumar Ch, Srinivas G, Kishor Kumar A, Praveen Kumar K, Vijay Kumar A. (2021). Real-time optical wireless mobile communication with high physical layer reliability Using GRA Method. J Comp Sci Appl Inform Technol. 6(1): 1-7. DOI: 10.15226/2474-9257/6/1/00149
22. Anand, L., Krishnan, M. M., Senthil Kumar, K. U., & Jeeva, S. (2020, October). AI multi agent shopping cart system based web development. In AIP Conference Proceedings (Vol. 2282, No. 1, p. 020041). AIP Publishing LLC.
23. KM, Z., Akhtaruzzaman, K., & Tanvir Rahman, A. (2022). BUILDING TRUST IN AUTONOMOUS CYBER DECISION INFRASTRUCTURE THROUGH EXPLAINABLE AI. International Journal of Economy and Innovation, 29, 405-428.
