• Arti
• Upasna Setia

The rapid growth of data-intensive applications has underscored the critical need for efficient, scalable, and adaptive data processing solutions within distributed cloud ecosystems. This paper proposes AI-driven optimization frameworks designed to address four fundamental challenges: computational efficiency, dynamic resource allocation, latency minimization, and effective handling of heterogeneous workloads. By leveraging machine learning models—particularly reinforcement learning and deep neural networks—these frameworks enable real-time adaptation to fluctuating workloads and infrastructure variability across cloud environments. Integrated predictive analytics anticipate resource demands and proactively balance loads, leading to significant reductions in processing time and energy consumption. Furthermore, graph-based workload modeling and federated learning mechanisms are incorporated to manage heterogeneous environments while ensuring data privacy across multi-cloud infrastructures. Experimental evaluations conducted on benchmark distributed systems demonstrate up to a 35% improvement in computational throughput and a 28% reduction in average latency. The results suggest that AI-augmented orchestration mechanisms are essential for the next generation of intelligent, elastic, and resilient cloud platforms capable of supporting modern big data and AI workloads at scale.

Distributed Cloud Ecosystems, AI-driven optimization, Computation Efficiency, Scalable Data Processing, Dynamic Resource Allocation

"Intelligent Optimization Strategies for Scalable Data Processing in Distributed Cloud Ecosystems", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.d471-d478, July-2025, Available :http://www.jetir.org/papers/JETIR2507354.pdf

"Intelligent Optimization Strategies for Scalable Data Processing in Distributed Cloud Ecosystems", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. ppd471-d478, July-2025, Available at : http://www.jetir.org/papers/JETIR2507354.pdf