Cross-Layer Artificial Intelligence Optimization for Real-Time Decision-Making in Intelligent Digital Systems

  • Authors

    • Bhuvaneshwari Department of Computer Science, Holy Cross College (Autonomous), India. Author

    DOI:

    https://doi.org/10.67228/3071561X/IJIRHT-V9I3P102

    Published 07-07-2026

  • Cross-Layer Optimization, Artificial Intelligence, Real-Time Decision-Making, Intelligent Digital Systems, Edge Computing, Cloud Computing, Multi-Objective Optimization, Adaptive Resource Allocation, Predictive Analytics, Latency-Aware Computing

    Issue

    Vol. 9 No. 3 (2026)

    Section

    Articles

  • Abstract

    This paper proposes a cross-layer AI optimization framework for real-time decision-making in intelligent digital systems. It integrates application requirements, data processing, network conditions, computing resources, and model performance to reduce latency, energy consumption, and resource inefficiency. The framework combines predictive machine learning, state estimation, multi-objective optimization, and feedback-driven adaptation to support rapid decisions and system-wide coordination. A literature-based comparison of independent optimization, rule-based coordination, and AI-enabled cross-layer control highlights key challenges, including heterogeneous workloads, dynamic networks, explain ability, privacy, and model drift. The study provides a foundation for applications in edge-cloud computing, IoT, transportation, healthcare, and enterprise analytics. Further empirical testing is needed to validate the framework’s effectiveness.

  • References

    [1] Davis, R. I., & Burns, A. (2011). A survey of hard real-time scheduling for multiprocessor systems. ACM Computing Surveys, 43(4), Article 35. https://doi.org/10.1145/1978802.1978814

    [2] Nangi, P. R., Obannagari, C. K. R. N., & Settipi, S. (2022). Enhanced Serverless Micro-Reactivity Model for High-Velocity Event Streams within Scalable Cloud-Native Architectures. International Journal of Emerging Research in Engineering and Technology, 3(3), 127-135.

    [3] Akinapalli, S. (2025). Metadata-driven data integration framework: Automating enterprise data integration through declarative approaches. European Modern Studies Journal, 9(4), 9. http://www.journal-ems.com

    [4] Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197. https://doi.org/10.1109/4235.996017

    [5] Obannagari, C. K. R. N., & Nangi, P. R. (2021). Predictive Autoscaling for Kubernetes Using Multivariate Time-Series Forecasting. International Journal of Emerging Research in Engineering and Technology, 2(2), 73-83.

    [6] Kreutz, D., Ramos, F. M. V., Veríssimo, P. E., Rothenberg, C. E., Azodolmolky, S., & Uhlig, S. (2015). Software-defined networking: A comprehensive survey. Proceedings of the IEEE, 103(1), 14–76. https://doi.org/10.1109/JPROC.2014.2371999

    [7] Virendra Jangid. (2023). Event-Driven Architectures for Large-Scale Financial Platforms. International Journal of Intelligent Systems and Applications in Engineering, 11(11s), 1124 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/8348

    [8] Nangi, P. R., & Obannagari, R. N. CK, & Settipi, S.(2022). Predictive SQL Query Tuning Using Sequence Modeling of Query Plans for Performance Optimization. International Journal of AI, BigData, Computational and Management Studies, 3(2), 104-113.

    [9] Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems (Vol. 30). Curran Associates, Inc.

    [10] Mullapudi, P. N., Bhaduri, S., Casadei, A., Guo, L., Wang, H., Ashok, V., ... & Rakshit, A. (2025). An end-to-end Causal Modeling Framework for Advanced Attribution in Supply Chain Operations. In KDD 2025 Workshop on AI for Supply Chain: Today and Future.

    [11] Vasantha Kumar Hosahalli Seenappa. (2025). Predictive Configuration Governance for AI-Assisted Network Automation in Hyperscale MPLS/IP Backbone Networks. International Journal of Intelligent Systems and Applications in Engineering, 13(2s), 352–358. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/8534

    [12] Obannagari, C. K. R. N., & Nangi, P. R. (2021). Predictive Database Performance Optimization Using Machine Learning-Driven Query Workload Modeling. International Journal of AI, BigData, Computational and Management Studies, 2(4), 107-116.

    [13] Y. Syed, “Enterprise Observability and Software Delivery Performance: From Telemetry to Engineering Intelligence”, IJADSMC, vol. 5, no. 2, pp. 01–08, Sep. 2022, doi: 10.67228/30713498/IJADSMC-2022PIIKP2M.

    [14] K. Bhagyasri, Education vs Entertainment on the mobile screen time spent per day by students in Higher Education in Tamil Nadu, Salud, Ciencia y Tecnologia - Serie de Conferencias, vol. 3, no. 714, pp. 1-11, 2024.

    [15] Nangi, P. R., Obannagari, C. K. R. N., & Settipi, S. (2022). Self-auditing deep learning pipelines for automated compliance validation with explainability, traceability, and regulatory assurance. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(1), 133-142.

    [16] Casadei, A., Bhaduri, S., Malshe, R., Mullapudi, P., Ratan, R., Pole, A., & Rakshit, A. (2025). Scaling Causal Mediation for Complex Systems: A Framework for Root Cause Analysis. arXiv preprint arXiv:2512.14764.

    [17] Mao, Y., You, C., Zhang, J., Huang, K., & Letaief, K. B. (2017). A survey on mobile edge computing: The communication perspective. IEEE Communications Surveys & Tutorials, 19(4), 2322–2358. https://doi.org/10.1109/COMST.2017.2745201

    [18] Nangi, P. R., & Settipi, S. (2023). A cloud-native serverless architecture for event-driven, low-latency, and AI-enabled distributed systems. International Journal of Emerging Research in Engineering and Technology, 4(4), 128-136.

    [19] Pavan Nithin Mullapudi (2024). An Adaptive Explainability Framework for Machine Learning Predictions of Deals in Cloud Computing . International Journal of Multidisciplinary Research and Growth Evaluation (IJMRGE), 5(3), 1027-1034. DOI: https://doi.org/10.54660/.IJMRGE.2024.5.3.1027-1034

    [20] Mijumbi, R., Serrat, J., Gorricho, J.-L., Bouten, N., De Turck, F., & Boutaba, R. (2016). Network function virtualization: State-of-the-art and research challenges. IEEE Communications Surveys & Tutorials, 18(1), 236–262. https://doi.org/10.1109/COMST.2015.2477041

    [21] Nangi, P. R. (2022). Multi-Cloud Resource Stability Forecasting Using Temporal Fusion Transformers. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(3), 123-135.

    [22] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., & Hassabis, D. (2015). Human-level control through deep reinforcement learning. Nature, 518, 529–533. https://doi.org/10.1038/nature14236

    [23] Reddy Nangi, P., & Reddy Nala Obannagari, C. K. (2023). Scalable End-to-End Encryption Management Using Quantum-Resistant Cryptographic Protocols for Cloud-Native Microservices Ecosystems. International Journal of Emerging Trends in Computer Science and Information Technology, 4(1), 142-153.

    [24] Syed, Y. (2023). Polyglot Persistence and Enterprise Data Migration: Architectures, Consistency, and Practice. American International Journal of Computer Science and Technology, 5(3), 61-66. https://doi.org/10.63282/3117-5481/AIJCST-V5I3P106

    [25] Mullapudi, P. N. (2025). Pricing optimization across domains: A comparative review. International Journal of Emerging Research in Engineering and Technology, 6(3), 16-33.

    [26] Nangi, P. R., & Obannagari, R. N. CK, & Settipi, S.(2023). A Multi-Layered Zero-Trust Security Framework for Cloud-Native and Distributed Enterprise Systems Using AI-Driven Identity and Access Intelligence. International Journal of Emerging Trends in Computer Science and Information Technology, 4(3), 144-153.

    [27] Gantikota, S. (2026). Securing Microservice Communication across WCF, JAX-RS, and Spring Boot: Authentication, Authorization, and Audit Patterns for Healthcare Interoperability. American International Journal of Computer Science and Technology, 8(2), 15-20. https://doi.org/10.63282/3117-5481/AIJCST-V8I2P102

    [28] S. Suganya, Dr K. Govind, "Relationship Between Social Media Addiction and Self-Esteem among Working and Non Working Women," Purakala, vol. 31, no. 5, pp. 29-33, 2020.

    [29] Nangi, P. R., & Obannagari, C. K. R. N. (2024). High-Performance Distributed Database Partitioning Using Machine Learning-Driven Workload Forecasting and Query Optimization. American International Journal of Computer Science and Technology, 6(2), 11-21.

    [30] Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). “Why should I trust you?” Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1135–1144). Association for Computing Machinery. https://doi.org/10.1145/2939672.2939778

    [31] Nangi, P. R., Obannagari, C. K. R. N., & Settipi, S. (2025). Quantum-Enhanced Optimization Models for Large-Scale Security Policy Evaluation in Distributed Cloud-Native Systems. American International Journal of Computer Science and Technology, 7(6), 46-56.

    [32] Syed, Y. (2025). Generative AI Integration and RAG-Based Enterprise Tooling: Architectures, Techniques, and Organizational Impact. International Journal of Scientific Research in Science, Engineering and Technology, 12(6), 600-606. DOI: https://doi.org/10.32628/IJSRSET2613255

    [33] 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

    [34] Nangi, P. R., & Obannagari, C. K. R. N. (2026). Federated ai-driven query optimization for distributed cloud databases. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 7(1), 91-99.

    [35] Sunkara, R. (2024). Hardware-in-the-Loop Power Profiling Automation for Consumer Streaming Devices: A Multi-Lab Framework for Regulatory Compliance Validation. International Journal of Emerging Trends in Computer Science and Information Technology, 5(4), 187-191. https://doi.org/10.63282/3050-9246.IJETCSIT-V5I4P121

    [36] Syed, Y. (2024). DevSecOps and Enterprise-Scale CI/CD Pipeline Engineering: Governing Delivery Velocity, Embedded Security, and Release Automation Across Polyglot Environments. International Journal of Scientific Research in Science, Engineering and Technology, 11(5), 475-484. DOI: https://doi.org/10.32628/IJSRSET2513156

    [37] Zhou, Z., Chen, X., Li, E., Zeng, L., Luo, K., & Zhang, J. (2019). Edge intelligence: Paving the last mile of artificial intelligence with edge computing. Proceedings of the IEEE, 107(8), 1738–1762. https://doi.org/10.1109/JPROC.2019.2918951

    [38] Brahmandam, L. M. K. (2025). A Methodology for Consolidating Decades-Old Enterprise Software Portfolios into a Unified Web Platform: Discovery, Data Model Unification, Architecture, and Migration Approach. American International Journal of Computer Science and Technology, 7(2), 112-121. https://doi.org/10.63282/3117-5481/AIJCST-V7I2P109

    [39] Nangi, P. R., Obannagari, C. K. R. N., & Settipi, S. (2025). AI-Driven Data Engineering Pipelines for High-Velocity Analytics Using Serverless Architectures and Event-Stream Intelligence. International Journal of AI, BigData, Computational and Management Studies, 6(3), 74-83.

  • Downloads