Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
Self-Healing AI-Native Real-Time Data Pipelines: Autonomous Resilience For Large-Scale Streaming Systems. |
| Συγγραφείς: |
Rajamani, Yogesh Pugazhendhi Duraisamy |
| Πηγή: |
Journal of International Crisis & Risk Communication Research (JICRCR); 2026, Vol. 9 Issue 1, p440-449, 10p |
| Θεματικοί όροι: |
Data pipelining, Streaming technology, Real-time computing, Automatic control systems, Anomaly detection (Computer security), Failure analysis |
| Περίληψη: |
In large streaming platforms today, there are common operational issues, such as data drift, throughput degradation, partition imbalance, and cascading failures, that impact availability and performance. Existing monitoring and rule-based automatic remediation solutions are unsuitable for workloads with millisecond-level latency and high availability needs. This article introduces a fully self-healing AI-native real-time data pipeline that integrates machine learning into the control plane of the streaming platform. It presents an end-to-end architecture that leverages graph neural networks and transformers for hybrid anomaly detection, LSTM-based predictive fault modeling, and reinforcement learning-based agents that autonomously select the best remediation policy (e.g., dynamic resource scaling, partition rebalancing, and dataflow rerouting). The framework implements continuous healing based on the detect-diagnose-predict-decide-act-verify-learn loop. Evaluating the framework with synthetic and real-world high-throughput streaming workloads shows improvements in downtime, latency, fault domains, and resource utilization to establish a new model of autonomous stream processing infrastructures that can continue to operate mission-critical workloads in cloud, hybrid, and edge environments. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: |
Complementary Index |