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@article{206077,
        author = {Tamilmani C and Kishore and Sourabh Kiran Keragute and Dr S.Nagamani},
        title = {AI-Driven Predictive Orchestration and Self-Healing in Distributed Real-Time Data Pipelines},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {2},
        pages = {169-173},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206077},
        abstract = {The explosion of big data and the need for real-time processing requires distributed systems that can deliver massive throughput with sub-second latency. In this paper, we introduce an AI-driven framework for predictive orchestration and self-healing of distributed data pipelines. Together, the framework combines Long Short-Term Memory (LSTM) networks for extreme event forecasting, Machine Learning (ML) optimized refresh-ahead caching, and dynamic load balancing within orchestration engines to move distributed architectures from reactive recovery to proactive automation. Empirical evaluations demonstrate end-to-end latency reductions of greater than 71%, 80% alert noise suppression, 15–30 minutes proactive failure prediction, and sustained system availability of 99.999% (five nines). These results provide a highly scalable, fault-tolerant foundation for next generation analytics platforms.},
        keywords = {AI Orchestration, Big Data, Distributed Systems, Fault Tolerance, LSTM Networks, Predictive Caching, Real-Time Analytics, Self-Healing Infrastructure, Stream Processing.},
        month = {July},
        }