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

Abstract

In the era of big data, the demand for scalable, efficient, and real-time data processing frameworks has surged, especially within cloud-native environments. Traditional graph processing methods face challenges in keeping pace with the dynamic nature of big data and real-time decision-making requirements. This paper explores the integration of artificial intelligence (AI) techniques with cloud-native graph processing frameworks to optimize the processing of largescale graphs in real time. By incorporating machine learning algorithms, such as reinforcement learning (RL) and graph neural networks (GNNs), into the graph processing workflows, this study introduces a novel approach that enhances the efficiency, adaptability, and scalability of graphbased operations in cloud environments. The proposed AI-powered framework is designed to facilitate real-time decision-making by dynamically adjusting resources and task allocation, ensuring minimal latency and maximal throughput. Results from experimental evaluations in simulated big data pipelines demonstrate the framework's ability to outperform traditional approaches, delivering improved response times, resource utilization, and predictive capabilities for graph analytics in large-scale cloud deployments. This paper provides significant contributions to advancing cloud-native graph processing technologies, empowering more intelligent and responsive systems for real-time big data applications.