Reinforcement Learning-Based Resilience Optimization and Predictive Threat Mitigation for SAP Cloud Infrastructure
DOI:
https://doi.org/10.21590/Keywords:
Reinforcement Learning, Resilient Cloud Infrastructure, SAP Systems, Predictive Threat Mitigation, Cloud Security, Intelligent Resource Management, Anomaly Detection, SAP S/4HANA, Hybrid Cloud, Autonomous Infrastructure RecoveryAbstract
The increasing adoption of cloud-based SAP enterprise systems has created a growing demand for resilient infrastructure management, intelligent resource optimization, and proactive cybersecurity. Traditional cloud management approaches often depend on predefined rules, reactive incident handling, and static resource allocation, limiting their ability to respond effectively to dynamic workloads and emerging cyber threats. This research proposes a reinforcement learning (RL)-based framework for enhancing cloud infrastructure resilience and predictive threat mitigation across SAP enterprise environments. The framework integrates adaptive resource management, predictive threat intelligence, anomaly detection, and automated recovery mechanisms to improve system availability, operational efficiency, and security responsiveness. Reinforcement learning agents continuously evaluate infrastructure conditions, workload behavior, resource utilization, and security telemetry to determine appropriate optimization and mitigation actions. The proposed approach supports SAP S/4HANA, SAP Business Technology Platform, and hybrid cloud deployments by incorporating workload forecasting, risk-aware decision-making, and policy-based infrastructure orchestration. Predictive threat mitigation identifies potentially harmful activities through machine learning-assisted analysis, enabling preventive responses before incidents significantly affect enterprise services. The methodology evaluates resilience using availability, recovery time, threat detection accuracy, resource utilization, response latency, and operational overhead. Comparative experiments against rule-based and conventional machine learning approaches are proposed to assess effectiveness. The framework aims to establish an adaptive, explainable, and secure infrastructure management strategy that strengthens SAP system continuity while supporting scalable cloud operations, intelligent automation, and proactive enterprise cybersecurity.
References
Xiu, X., Li, J., Long, Y., et al. (2023). MRLCC: An adaptive cloud task scheduling method based on meta reinforcement learning. Journal of Cloud Computing, 12, Article 75. https://doi.org/10.1186/s13677-023-00440-8
Vani, M. (2026). Adaptive agentic AI frameworks for autonomous task planning and parallel execution. In 2026 International Artificial Intelligence Technology Conference (AITC) (pp. 78–84). IEEE. https://doi.org/10.1109/AITC70732.2026.11666578
Mysula, S. (2023). Performance optimization of distributed enterprise integration workflows across heterogeneous applications. International Journal of Engineering & Extended Technologies Research, 5(1), 6003–6008.
Hossain, M. S., & Rahaman, M. M. (2026). A Comprehensive Cyber Security Architecture for Government Agencies based on the AITIR Framework. Journal of Intelligent Decision Making and Information Science, 3(9s), 1248-1266.
Polamarasetty, V. K. (2021). Modernizing SAP sales and distribution systems through ABAP-based enterprise solutions. International Journal of Research and Applied Innovations, 4(2), 4925-4930.
Karrothu, A. (2025). Engineering scalable cloud-native distributed systems for real-time security telemetry ingestion and threat detection. Journal of Computational Analysis and Applications, 34(12), 1175–1188.
Shaik, N. (2025). Dynamic healthcare intelligence: Integrating AI predictive analytics with Kubernetes scaling for enhanced patient outcomes. International Journal of Engineering & Extended Technologies Research, 7(2), 9715–9720.
Nelavala, M. M. (2026). Protocol-Aware Chaos Engineering for SWIFT Gpi-Compatible Microservices: A Framework for Five-Nines Cross-Border Payment Resilience. Journal of Intelligent Decision Making and Information Science, 3(10s), 621-637.
Gunasekaran, R. M. N. (2026). Intelligent Systems for Regulatory Compliance: Cyber Resilience in a High Risk Financial Landscape. American International Journal of Computer Science and Technology, 7-27.
Ahuja, D. (2026, April). Declarative Multi-Cluster Kubernetes Deployment Architecture Using GitOps. In 2026 International Symposium of Systems, Advanced Technologies and Knowledge (ISSATK) (pp. 1-6). IEEE.
Kasarapu, B. C. (2026). Automated Quality Assurance in Web Applications Using Model-Based Testing and Cypress with AI-Assisted Test Generation. Emerging Innovations Frontiers: Science, Technology, and Social Studies, 14-23.
Venkatasalam, K., Rajendran, P., & Thangavel, M. (2019). Improving the accuracy of feature selection in big data mining using accelerated flower pollination (AFP) algorithm. Journal of medical systems, 43(4), 96.
Hossain, I., Hossain, M. S., Rasul, I., Prince, N. U., Datta, A., & Akand, A. R. (2026, June). Machine Learning-Based Evaluation of Password Security and User Awareness for Cyber Risk Prevention. In 2026 Third International Conference on Innovations in Cybersecurity and Data Science (ICICDS) (pp. 499-504). IEEE.
Kalabhavi, V. (2024). Promotional spend analytics using AI for PFME allocation and coupon redemption optimization. International Journal of Research Publications in Engineering, Technology and Management, 7(2), 10417–10429.
Chundi, V. R. K. (2026). An Agentic ERP Governance Framework for Autonomous AI Agent Deployment in Cloud-Based Industrial Management Systems. International Journal of Computer Information Systems and Industrial Management Applications, 18(21s), 500-511.
Ramasamy, M. (2023). Cloud-native control plane design for modern network automation systems. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(4), 9082–9091.
Sazu, A. H., Khan, M. A. N., Sultana, S., Tasnim, M., Jakir, T., Alam, A., … Himeluzzaman, M. (2025). Artificial intelligence-based anomaly detection for environmental and financial risk management in banking. International Journal of Advances in Signal and Image Sciences, 2083–2095.
Tarigoppula, S. (2024). Autonomous cloud operations: Integrating AIOps with AWS infrastructure for predictive incident prevention. International Journal of Engineering & Extended Technologies Research, 6(2), 7956–7967.
Bitragunta, S. L. V. (2022). High level modeling of high-voltage gallium nitride (GaN) power devices for sophisticated power electronics applications. Journal of Artificial Intelligence, Machine Learning and Data Science, 1(1), 2011-2015.
Selvarajan, K. (2025). Next-generation AI-driven big data platforms. International Journal of Research Publications in Engineering, Technology and Management, 8(2), 11831–11836.
Chinnam, N. B. (2025). Intelligent distribution center integration for transportation modernization. International Journal of Research and Applied Innovations, 8(2), 11283–11288.
Neela, S. (2024). Low-latency, high-throughput middleware for real-time enterprise integration: Technical design, societal impact, and policy considerations. International Journal of Emerging Research in Engineering and Technology, 5(3), 191–200. https://doi.org/10.63282/3050-922X.IJERET-V5I3P121
Reddy, K. V. (2023). Reconfigurable FPGA-based acceleration of RTL simulation and co-simulation for cloud datacenter workloads. International Journal of Humanities and Information Technology, 5(1), 104–118.
Aragani, V. M., Maroju, P. K., & Mudunuri, L. N. R. (2024, December). Implementing Non-functional Production Regression Testing with Kubernetes. In International Conference on Information and Communication Technology for Competitive Strategies (pp. 231-244). Singapore: Springer Nature Singapore.
Padmanabham, S., Khokhar, J., & Bhandari, R. S. (2025, November). Cluster Analysis and Latent Class Modelling for Consumer Segmentation in Digital Ecosystem. In 2025 International Conference on Computational Engineering, Sensing Technology and Management (ICCETM) (pp. 1-5). IEEE.
Gullapalli, S., & Divi, V. R. (2026, July). Kairos: SLO-Aware Scheduling and a Benchmark for Multi-Tenant LLM Inference Serving under Bursty, Heterogeneous Load. In 2026 5th International Conference on Electronics Representation and Algorithm (ICERA) (pp. 598-603). IEEE.
Muppidi, N. K. R. (2026, July). Secure Context Cache: Token-Efficient and Least-Privilege Shared Memory for Enterprise Developer Agents. In 2026 5th International Conference on Electronics Representation and Algorithm (ICERA) (pp. 679-684). IEEE.
Kari, M. (2025). AI-Assisted Query Optimization Techniques for Cloud Databases Supporting Hybrid SQL and NoSQL Workloads. International Journal of Emerging Research in Engineering and Technology, 6(4), 62-71.
Kopparthi, G. S. (2025). Cost Optimization in Azure and AWS Cloud. Journal of Marketing & Social Research, 2, 177-182.
Yadavalli, V. R. (2023). Cutover intelligence mesh: A dependency-aware architecture for enterprise cloud ERP migration. International Journal of Computer Technology and Electronics Communication, 6(4), 7400–7416.
Chinthamani, R. (2025). Policy-as-code for automated enterprise production stability and deployment governance. International Journal of Computer Technology and Electronics Communication, 8(2), 10503–10508.
Kundavaram, R. R., & Onteddu, A. R. (2023). Enhancing automotive performance and real-time data analytics through cloud computing. Journal of Computational Analysis and Applications (JoCAAA), 31(4), 1382–1392.
Shaik, N. (2023). Zero downtime, zero trust: Automating certificate security at scale. International Journal of Engineering & Extended Technologies Research, 5(4), 6963–6978.
Chandran, A. S. (2026). Towards real-time cohort analytics at petabyte scale: A configuration-driven architecture for healthcare claims data. International Journal of Research Publications in Engineering, Technology and Management, 9(2), 840–849.
Madabathula, L. (2025). A governance-driven enterprise lakehouse architecture for real-time analytics and machine learning enablement. International Journal of Applied Mathematics, 38(12s), 5022–5033.
Puram, S. (2021). Reliable and Scalable Mobile Technology Architecture: A Formal Model, Synchronization Protocol, and Reference Design. The USA Journals TAJET, 3(08), 33-48.
Seknametla, P. R., & Sunkara, R. (2025). Applying AIOps for Predictive Incident Management in DevOps-Driven Cloud Infrastructure. International Journal, 12(6).
Parupalli, A. (2025, August). Improving Cloud Resource Utilization and Cost Efficiency Through Data-Driven ML Algorithms. In 2025 Global Conference on Information Technology and Communication Networks (GITCON) (pp. 1-8). IEEE.
Basavala, S. R. (2024). Automated vulnerability intelligence and risk prioritization for secure CI/CD pipeline engineering. International Journal of Research and Applied Innovations, 7(1), 10175–10185.
