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Research Paper | Computer Science | Volume 15 Issue 4, April 2026 | Pages: 1257 - 1259 | United States
Generative AI Driven Autonomous Optimization of Distributed Data Processing Systems in Enterprise Environments
Abstract: Distributed data processing systems are essential for large-scale analytics, operational intelligence, scientific computing, and enterprise reporting. Although modern platforms provide elasticity and scale, organizations continue to face high operational cost, recurring failures, performance bottlenecks, fragmented observability, and increasing engineering complexity. This paper proposes a governed Generative Artificial Intelligence framework that analyzes telemetry, metadata, and historical operational patterns to optimize distributed workloads. The proposed model improves runtime efficiency, reduces infrastructure spend, accelerates incident resolution, and enhances engineering productivity while maintaining enterprise controls.
Keywords: Generative AI, Distributed Computing, Big Data, Data Engineering, Autonomous Optimization, AIOps
How to Cite?: Kimi Pawar, "Generative AI Driven Autonomous Optimization of Distributed Data Processing Systems in Enterprise Environments", Volume 15 Issue 4, April 2026, International Journal of Science and Research (IJSR), Pages: 1257-1259, https://www.ijsr.net/getabstract.php?paperid=SR26420071926, DOI: https://dx.dx.doi.org/10.21275/SR26420071926