Machine Learning-Enabled Self-Healing Data Pipelines: An Autonomous Architecture for Failure Detection, Diagnostic Reasoning, and Automated Remediation
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V6I6P110Keywords:
Self-Healing Data Pipelines, AIOps, DataOps, MLOps, Anomaly Detection, Root Cause Analysis, Automated Remediation, Data Observability, Pipeline Reliability, Diagnostic ReasoningAbstract
Enterprise data pipelines have become essential infrastructure for analytics, artificial intelligence, risk management, digital services, and regulatory reporting. However, their reliability is challenged by issues such as schema drift, source delays, data corruption, orchestration failures, resource constraints, model drift, and downstream inconsistencies. This paper proposes an autonomous architecture for machine learning-enabled self-healing data pipelines that integrates multimodal observability, anomaly detection, diagnostic reasoning, and policy-driven automated remediation. The architecture views data pipelines as continuously monitored socio-technical systems represented through metrics, logs, traces, lineage graphs, data contracts, quality indicators, execution histories, and incident records. The proposed framework consists of five layers: (1) a telemetry and lineage substrate, (2) a feature and knowledge representation layer, (3) a detection layer combining statistical, rule-based, and machine learning techniques, (4) a diagnostic reasoning layer using causal and graph-based methods, and (5) a remediation layer that executes controlled repair actions under governance policies. The study further introduces a fault taxonomy, a remediation safety model, and an evaluation framework based on fault injection experiments. Performance is assessed using metrics such as detection precision, diagnosis accuracy, mean time to recovery, false remediation rate, data quality preservation, and auditability. The paper argues that self-healing should follow a constrained autonomy model, where automated actions are permitted only when supported by sufficient evidence, limited operational risk, validated rollback mechanisms, and explicit policy approval. By integrating AIOps, DataOps, MLOps, and software governance practices, the proposed architecture provides a research-driven blueprint for building resilient data platforms capable of early failure detection, systematic root-cause analysis, automated remediation, and evidence-based escalation.
References
[1] D. Baylor, E. Breck, H.-T. Cheng, N. Fiedel, C. Y. Foo, Z. Haque, S. Haykal, M. Ispir, V. Jain, L. Koc, C. Y. Koo, L. Lew, C. Mewald, A. N. Modi, N. Polyzotis, S. Ramesh, S. Roy, S. E. Whang, M. Wicke, J. Wilkiewicz, X. Zhang, and M. Zinkevich, "TFX: A TensorFlow-Based Production-Scale Machine Learning Platform," in Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, 2017, pp. 1387-1395, https://doi.org/10.1145/3097983.3098021.
[2] S. K. Gunda, "Comparative Analysis of Machine Learning Models for Software Defect Prediction," 2024 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS), Chennai, India, 2024, pp. 1-6, https://doi.org/10.1109/ICPECTS62210.2024.10780167.
[3] N. Polyzotis, S. Roy, S. E. Whang, and M. Zinkevich, "Data Management Challenges in Production Machine Learning," in Proceedings of the 2017 ACM International Conference on Management of Data, Chicago, IL, USA, 2017, pp. 1723-1726, https://doi.org/10.1145/3035918.3054782.
[4] Sivva, S. D., Thalakanti, R. R., Bandari, S. S. G., & Yettapu, S. D. R. (2023). AI-Driven Decision Intelligence for Agile Software Lifecycle Governance: An Architecture-Centered Framework Integrating Machine Learning Defect Prediction and Automated Testing. International Journal of Emerging Trends in Computer Science and Information Technology, 4(4), 167-172. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I4P118.
[5] V. Chandola, A. Banerjee, and V. Kumar, "Anomaly Detection: A Survey," ACM Computing Surveys, vol. 41, no. 3, Article 15, pp. 1-58, 2009, https://doi.org/10.1145/1541880.1541882.
[6] S. Shankar and A. G. Parameswaran, "Towards Observability for Production Machine Learning Pipelines," Proceedings of the VLDB Endowment, vol. 15, no. 13, pp. 4015-4022, 2022, https://doi.org/10.14778/3565838.3565853.
[7] Gunda SK, Yettapu SDR, Bodakunti S, Bikki SB. Decision Intelligence Methodology for AI-Driven Agile Software Lifecycle Governance and Architecture-Centered Project Management, 2023 Mar. 30;4(1):102-8. https://doi.org/10.63282/3050-9262.IJAIDSML-V4I1P112.
[8] E. Breck, N. Polyzotis, S. Roy, S. E. Whang, and M. Zinkevich, "Data Validation for Machine Learning," Proceedings of Machine Learning and Systems, vol. 1, pp. 334-347, 2019.
[9] P. Notaro, J. Cardoso, and M. Gerndt, "A Survey of AIOps Methods for Failure Management," ACM Transactions on Intelligent Systems and Technology, vol. 12, no. 6, Article 81, pp. 1-45, 2021, https://doi.org/10.1145/3483424.
[10] Balerao, M. (2023). A converged artificial intelligence architecture for innovation, software lifecycle optimization, and cybersecurity risk mitigation. International Journal of Multidisciplinary Futuristic Development, 4(1), 117-120. https://doi.org/10.54660/IJMFD.2023.4.1.117-120.
[11] M. Du, F. Li, G. Zheng, and V. Srikumar, "DeepLog: Anomaly Detection and Diagnosis from System Logs through Deep Learning," in Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, Dallas, TX, USA, 2017, pp. 1285-1298, https://doi.org/10.1145/3133956.3134015.
[12] Gunda, S. K. G. (2023). The Future of Software Development and the Expanding Role of ML Models. International Journal of Emerging Research in Engineering and Technology, 4(2), 126-129. https://doi.org/10.63282/3050-922X.IJERET-V4I2P113.
[13] D. Xin, H. Miao, A. Parameswaran, and N. Polyzotis, "Production Machine Learning Pipelines: Empirical Analysis and Optimization Opportunities," in Proceedings of the 2021 International Conference on Management of Data, Virtual Event, China, 2021, pp. 2639-2652, https://doi.org/10.1145/3448016.3457566.
[14] Sivva, S. D. (2023). An end-to-end AI-based systems engineering paradigm for lifecycle governance, predictive quality assurance, automation economics, and cybersecurity intelligence. Journal of Frontiers in Multidisciplinary Research, 4(1), 600-604. https://doi.org/10.54660/.JFMR.2023.4.1.600-604.
[15] T. Akidau, R. Bradshaw, C. Chambers, S. Chernyak, R. Fernandez-Moctezuma, R. Lax, S. McVeety, D. Mills, F. Perry, E. Schmidt, and S. Whittle, "The Dataflow Model: A Practical Approach to Balancing Correctness, Latency, and Cost in Massive-Scale, Unbounded, Out-of-Order Data Processing," Proceedings of the VLDB Endowment, vol. 8, no. 12, pp. 1792-1803, 2015, https://doi.org/10.14778/2824032.2824076.
[16] S. K. Gunda, "Analyzing Machine Learning Techniques for Software Defect Prediction: A Comprehensive Performance Comparison," 2024 Asian Conference on Intelligent Technologies (ACOIT), KOLAR, India, 2024, pp. 1-5, https://doi.org/10.1109/ACOIT62457.2024.10939610.
[17] D. Sculley, G. Holt, D. Golovin, E. Davydov, T. Phillips, D. Ebner, V. Chaudhary, M. Young, J.-F. Crespo, and D. Dennison, "Hidden Technical Debt in Machine Learning Systems," in Advances in Neural Information Processing Systems 28, 2015, pp. 2503-2511.
[18] S. Shankar, R. Garcia, J. M. Hellerstein, and A. G. Parameswaran, "Operationalizing Machine Learning: An Interview Study," arXiv:2209.09125, 2022.
[19] B. Burns, B. Grant, D. Oppenheimer, E. Brewer, and J. Wilkes, "Borg, Omega, and Kubernetes," ACM Queue, vol. 14, no. 1, pp. 70-93, 2016, https://doi.org/10.1145/2898442.2898444.
[20] N. Sambasivan, S. Kapania, H. Highfill, D. Akrong, P. K. Paritosh, and L. M. Aroyo, "Everyone Wants to Do the Model Work, Not the Data Work: Data Cascades in High-Stakes AI," in Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, Yokohama, Japan, 2021, Article 39, pp. 1-15, https://doi.org/10.1145/3411764.3445518.
[21] M. Armbrust, T. Das, J. Torres, B. Yavuz, S. Zhu, R. Xin, A. Ghodsi, I. Stoica, and M. Zaharia, "Structured Streaming: A Declarative API for Real-Time Applications in Apache Spark," in Proceedings of the 2018 International Conference on Management of Data, Houston, TX, USA, 2018, pp. 601-613, https://doi.org/10.1145/3183713.3190664.
[22] E. Breck, S. Cai, E. Nielsen, M. Salib, and D. Sculley, "The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction," in Proceedings of the 2017 IEEE International Conference on Big Data, Boston, MA, USA, 2017, pp. 1123-1132.
[23] M. Zaharia, M. Chowdhury, M. J. Franklin, S. Shenker, and I. Stoica, "Spark: Cluster Computing with Working Sets," in Proceedings of the 2nd USENIX Workshop on Hot Topics in Cloud Computing, Boston, MA, USA, 2010, pp. 10-10.
[24] S. Schmidl, P. Wenig, and T. Papenbrock, "Anomaly Detection in Time Series: A Comprehensive Evaluation," Proceedings of the VLDB Endowment, vol. 15, no. 9, pp. 1779-1797, 2022, https://doi.org/10.14778/3538598.3538602.
[25] L. Zhang, T. Jia, M. Jia, Y. Wu, A. Liu, Y. Yang, Z. Wu, X. Hu, P. S. Yu, and Y. Li, "A Survey of AIOps for Failure Management in the Era of Large Language Models," arXiv:2406.11213, 2024.
