Hybrid of Wait-for-Graph Analysis and Graph Neural Networks-an AI Enabled Hybrid Approaches for Deadlock Avoidance

Deadlock prevention remains a critical challenge in distributed computing systems, where traditional wait-for graph (WFG) analysis faces inherent limitations in scalability and adaptability to dynamic operational environments. This paper introduces a novel hybrid framework that integrates formal WFG analysis with graph neural networks (GNNs) to overcome these constraints. The proposed solution combines the theoretical rigor of classical cycle detection with the pattern recognition capabilities of modern deep learning architectures. We evaluate our approach through extensive experimentation on industry-standard benchmarks (TPC-C and YCSB) and controlled synthetic deadlock scenarios, demonstrating consistent performance improvements over conventional methods. The hybrid model achieves 92% detection accuracy while reducing false positives by 15% compared to established baselines, including the Chandy-Misra algorithm, pure GNN implementations, and timeout-based approaches. Our comprehensive analysis includes detailed architectural specifications, empirical validation through multiple performance metrics, and comparative visualizations that highlight the system’s efficiency gains. These results suggest significant practical implications for the design of next-generation distributed systems, particularly in scenarios requiring both reliability and scalability. The work contributes to the ongoing synthesis of formal methods and machine learning techniques in systems engineering.