AI-Powered Cloud-Native Graph Processing for Real-Time Decision-Making in Big Data Pipelines

AI-Powered Cloud-Native Graph Processing for Real-Time Decision-Making in Big Data Pipelines

Sai Kiran Reddy Malikireddy, Independent Researcher, USA

Abstract

This is the world where demand for scalable, efficient, real-time solutions to data processing problems in cloud-native environments is growing fast. Traditional graph processing frameworks frequently fail to meet the demands of either dynamic big data or real-time decision-making. A novel AI-driven framework integrating GNNs and RL into cloud-native graph processing is proposed in this paper. The proposed system increases scalability, adaptability, and efficiency in real-time decision-making with less latency and high utilization of resources. Experimental evaluations show that there are significant improvements in throughput, latency, and resource efficiency compared to traditional methods. This work opens up the door for advanced graph-based analytics in large-scale distributed systems and provides a clear direction for future research in the integration of AI in big data ecosystems.

Keywords

Cloud-native architectures, Graph analytics, AI-driven processing, Machine learning techniques, Big data systems, Reinforcement learning models, Graph neural networks (GNNs), Real-time analytics, Distributed systems, Scalability, Optimization, Dynamic resource allocation, AI in cloud computing.