Hybrid High Performance Cloud Oriented Big Data Processing Pipeline for Mobility Aware Predictive Analytics Multimedia Mining and Distributed Decision Making in Mobile Communication Systems

Abstract

This paper proposes a Hybrid High-Performance Cloud-Oriented Big Data Processing Pipeline focusing on the mobility-aware predictive analytics, multimedia mining and distributed decision-making in mobile communication systems. The former harvests cloud computer capacity and the latter leverages edge computing low-latency intelligence to build a joint data pipeline to deal with heterogeneous multimedia and mobility data stream in real time. It features multi-layered processing hierarchy, federated aggregation, adaptive offloading and AI-based predictive modelling to balance the system scalability and energy efficiency. It was deployed using Docker-based edge simulations, Kubernetes orchestration and public clouds AWS & GCP for big data analytics. Experimental results show 96.4% prediction accuracy, 38% reduction on latency and 22% improvement of energy efficiency when compared to the baseline cloud and federated models. The results show that the proposed model provides effective and efficient solution for big data processing in massive mobile networks.