Dynamic CAP Optimization in Distributed Databases via Adaptive Graph Neural Networks with Causal Inference

Abstract

This paper presents a framework that leverages advanced Graph Neural Networks (GNNs) to address funda- mental challenges in distributed database systems, particularly those constrained by the CAP theorem. The framework combines graph theory, distributed systems theory, and deep learning to create adaptive, self optimizing database architectures. Through extensive empirical evaluation across multiple real-world datasets and synthetic benchmarks, we demonstrate substantial improvements in consistency enforcement (45% latency reduction), availability optimization (58% load balancing improvement), and intelligent partitioning strategies (52% edge-cut reduction). The framework introduces hierarchical GNN based consistency prediction, multi-objective availability optimization through reinforcement learning-enhanced GNNs, and dynamic partitioning with causal inference-based graph embeddings. These contributions establish a new paradigm for intelligent distributed database management that adapts to evolving workloads while maintaining theoretical guarantees.