Explainable AI for Root Cause Analysis and Predictive Performance Optimization in Distributed Enterprise Applications
Abstract
Distributed enterprise applications operate across interconnected services, databases, cloud platforms, and network infrastructures, making performance management and root cause identification increasingly complex. Traditional monitoring approaches primarily rely on predefined thresholds, static rules, and reactive troubleshooting, which often fail to identify hidden dependencies and predict emerging failures. Explainable Artificial Intelligence (XAI) offers a promising approach by combining predictive machine learning models with interpretable explanations that clarify why performance degradation occurs and how corrective actions can be prioritized. This research explores an integrated XAI framework for root cause analysis and predictive performance optimization in distributed enterprise applications. The proposed methodology incorporates telemetry data, including application logs, distributed traces, infrastructure metrics, and service dependency information, to detect anomalies, predict performance degradation, and identify probable root causes. Explainability techniques, including SHapley Additive exPlanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), and feature importance analysis, are employed to improve transparency and operational trust. Predictive optimization mechanisms subsequently recommend resource allocation adjustments, workload balancing, and configuration improvements. The framework is evaluated through detection accuracy, root cause localization precision, prediction lead time, latency reduction, resource utilization, and explanation fidelity. By integrating predictive analytics with interpretable decision support, this research aims to improve incident resolution efficiency, operational reliability, and resource utilization while enabling enterprise engineers to make informed, evidence-based decisions in complex distributed computing environments
Article Information
Journal |
International Journal of Science, Research and Technology |
|---|---|
Volume (Issue) |
Vol. 7 No. 6 (2024): International Journal of Science, Research and Technology (IJSRAT) |
DOI |
|
Pages |
13347-13356 |
Published |
November 13, 2024 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Dr. Mabruk Fekihal (%2024). Explainable AI for Root Cause Analysis and Predictive Performance Optimization in Distributed Enterprise Applications. International Journal of Science, Research and Technology , Vol. 7 No. 6 (2024): International Journal of Science, Research and Technology (IJSRAT) , pp. 13347-13356. https://doi.org/10.15662/IJSRAT.2024.0706020 |
References
2. Sahu, S. K. (2024). Enterprise AI Governance Architecture for Salesforce-Based Healthcare CRM Platforms: A Pattern-Oriented Framework for Regulated AI. American International Journal of Computer Science and Technology, 6(5), 83–88.
3. Chinthamani, R. (2023). Engineering resilient microservices for real-time retail order and inventory synchronization. International Journal of Engineering & Extended Technologies Research, 5(6), 7774–7779.
4. Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud. International Journal of Business Intelligence and Data Mining, 15(3), 273–287.
5. Kalabhavi, V. (2024). Promotional spend analytics using AI for PFME allocation and coupon redemption optimization. International Journal of Emerging Trends in Engineering and Management Research, 9(2), 15394–15406.
6. 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.
7. Tarigoppula, S. (2023). Adaptive cloud cost optimization through predictive auto-scaling and infrastructure intelligence. International Journal of Future Innovative Science and Technology, 6(2), 10278–10290.
8. Chy, M. S. K., Enaye, M. A., Alam, A., Gazi, M. S., Siddique, M. T., Tasnim, M., & Sultana, S. (2024). Data-Driven Adaptive AI Cyber Defense: A Comparative Analysis of Machine Learning Classifiers for Malware and APT Detection. International Journal of Research and Applied Innovations, 7(4), 11217–11226.
9. Raja, G. V. (2020). Metadata gets a makeover: The machine learning approach. International Journal of Computer Technology and Electronics Communication, 3(6), 2900–2903.
10. Mysula, S. (2024). Event-driven integration architecture using publication methods for real-time DealCloud business operations & workflows. International Journal of Research and Applied Innovations, 7(4), 11233–11239.
11. Mathew, A. R. (2019). Cyber-infrastructure connections and smart gird security. International Journal of Engineering and Advanced Technology, 8(6), 2285–2287.
12. Seknametla, P. R., & Sunkara, R. (2024). Threat Modeling Integration in DevSecOps Pipelines: Early-Stage Security Risk Identification Using Shift-Left Approaches. International Journal of Emerging Research in Engineering and Technology, 5(1), 126–133.
13. Koganti, H. (2024). Beyond reactive scaling: A review of AI-driven proactive and context-aware auto-scaling in cloud-edge environments. International Journal of Engineering & Extended Technologies Research, 6(3), 8175–8183.
14. Bandhela, R. R. (2020). AI-driven cybersecurity: Proactive threat detection and intelligent response systems. Journal of Computational Analysis and Applications (JoCAAA), 28(6), 66–73.
15. Reddy, K. V. (2021). UVM methodology for industry-specific applications using AI in hardware verification. International Journal of Science, Research and Technology, 4(3), 5731–5744.
16. Neela, S. (2024). Middleware-based interoperability models for hybrid and multi-cloud enterprise environments. International Journal of Emerging Trends in Computer Science and Information Technology, 5(1), 195–210. https://doi.org/10.63282/3050-9246.IJETCSIT-V5I1P119
17. Gopinathan, V. R. (2022). Advanced Predictive Decision Intelligence Using Machine Learning for Strategic Enterprise Management Platforms. International Journal of Research and Applied Innovations, 5(6), 8162–8169.
18. Karrothu, A. (2024). Automated risk-based security controls for vulnerability detection and prevention across cloud-native software development lifecycles. International Research Journal of Innovative Engineering, 8(4), 14994–15009.
19. 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.
20. Chandran, A. S. (2024). Engineering billion-query search systems: Lessons from large-scale metadata platforms. International Journal of Research and Applied Innovations, 7(2), 10510–10519.
21. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
22. Yadavalli, V. R. (2023). Cutover intelligence mesh: A dependency-aware architecture for enterprise cloud ERP migration. International Journal of Future Innovative Science and Technology, 6(4), 11015–11031.
23. Bandari, U., & Thilagaraj, S. V. (2022). From central BI to federated self-service: A platform-as-a-product reference architecture for data mesh for a multinational manufacturer. International Journal of Science, Research and Technology, 5(3), 7784–7794.
24. Chinnam, N. B. (2022). Engineering real-time supply chain visibility and ETA intelligence platforms for large-scale retail distribution networks. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(6), 7918–7922.
25. Basavala, S. R. (2022). Security assessment framework for microservices-based applications using automated penetration testing. International Journal of Emerging Trends in Engineering and Management Research, 7(4), 12179–12190.
26. Sasikumar, B., Venkatasalam, K., & Rajendran, P. (2024). Induction motor fault detection and classification using rcnn and surf based machine learning algorithms and infrared thermography. Scientia Iranica.
27. Gajjela, H., & Kari, M. (2024). Secure Multi-Tenant AI Architecture Enhances Enterprise AI Adoption Through Tenant Trust Using PLS-SEM in India’s Salesforce Cloud Industry. International Journal of Engineering Science & Humanities, 14(2), 253–271.
28. Soundappan, S. J. (2022). Distributed Cloud Architecture for Intelligent Performance Optimization and Proactive Real-Time Threat Detection. International Research Journal of Innovative Engineering, 6(6), 11426–11432.
29. Selvarajan, K. (2022). Architecting scalable self-service data platforms for enterprise analytics. International Journal of Science, Research and Technology (IJSRAT), 5(2), 7427–7436.
30. Gunasekaran, R. M. N. (2024). From Data to Decisions: How Quality Drives Machine Learning Success. The Eastasouth Journal of Information System and Computer Science, 2(01), 120–130.
31. Sugumar, R. (2021). Generative AI Pipelines for Safety Validation of Autonomous Driving Models. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 4(5), 5466–5469.
32. Shaik, N. (2023). Zero downtime, zero trust: Automating certificate security at scale. International Journal of Emerging Trends in Engineering and Management Research, 8(4), 13988.
33. Kartheek Reddy Jinna, & Subramani, S. K. (2024). Private LLM serving on enterprise container platforms: A reference architecture for GPU sharing, version governance and policy-enforced rollouts in regulated industries. International Journal of Research Publications in Engineering, Technology and Management, 7(1), 10056–10066.
34. Vimal Raja, G. (2022). Leveraging machine learning for real-time short-term snowfall forecasting using multisource atmospheric and terrain data integration. International Journal of Multidisciplinary Research in Science, Engineering and Technology, 5(8), 1336–1339.
35. Ramasamy, M. (2022). Closed-loop network automation: Architectural principles from Catalyst Center-scale systems. International Research Journal of Innovative Engineering, 6(3), 10698–10708.
36. Padmanabham, S. (2024). AI assisted fraud investigation architecture patterns for technology controls in financial institutions. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(3), 10587–10592.
37. Kopparthi, G. S. (2022). AI in healthcare: Unlocking the potential of predictive analytics. Journal of Computational Analysis and Applications, 30(2), 1183–1193. https://eudoxuspress.com/index.php/pub/article/view/5508
38. Minna, F., & Massacci, F. (2023). SoK: Run-time security for cloud microservices. Are we there yet? Computers & Security, 127, Article 103119. https://doi.org/10.1016/j.cose.2023.103119