Predictive Deadlock Prevention for Safeguarding Societal Infrastructure and Economic Stability through Markov Models-Consensus Intelligence

Deadlocks remain a significant challenge in distributed and cloud-based computing systems, where concurrent processes compete for limited resources, potentially leading to service unavailability and cascading system failures. This issue is particularly critical in systems supporting societal infrastructure, where reliability and timely response are essential. Conventional deadlock handling techniques are largely reactive, detecting deadlocks only after system performance has already degraded. This limitation motivates the need for predictive approaches that can identify deadlock-prone states in advance. This research presents a predictive deadlock prevention framework based on probabilistic state modeling using Markov processes. System execution is represented as a sequence of state transitions derived from observed resource allocation and waiting behaviors. Transition probabilities are used to estimate the likelihood of entering deadlock-prone states, enabling early identification of high-risk conditions prior to deadlock formation. The proposed model is evaluated using experimentally generated system traces under varying levels of resource contention. Performance is assessed using classification accuracy, precision, and recall. The results show that the model achieves measurable predictive accuracy while maintaining a balanced ability to detect deadlock-prone states and limit false alarms. These findings indicate that probabilistic state-based modeling provides an interpretable and computationally lightweight foundation for proactive deadlock prevention. This work establishes a baseline predictive framework for deadlock management and highlights its potential for extension using more advanced learning techniques to improve prediction accuracy and scalability in complex cloud environments by safeguarding societal infrastructure.