Abstract:

Deadlock prevention and avoidance are critical challenges in operating systems and resource management. Traditional algorithms like the Banker's Algorithm provide theoretical solutions but lack predictive capabilities and real-time risk assessment. This paper presents an advanced deadlock avoidance system that integrates machine learning with classical resource management techniques.The implementation features a comprehensive graphical interface visualizing resource allocation graphs, safety sequences, and risk metrics. Experimental results demonstrate that our ML-enhanced approach can predict deadlocks with up to 87% accuracy, providing early warnings that enable proactive resource management decisions. This hybrid approach bridges the gap between theoretical deadlock avoidance and practical system implementation, offering a more intelligent and adaptive solution for modern computing environments.