REAL-TIME DEADLOCK DETECTION AND RECOVERY

Deadlock is a critical issue in real-time systems where tasks compete for shared resources under strict timing constraints. The presence of a deadlock can lead to unacceptable delays or system failure, especially in safety-critical applications such as aviation, healthcare, and industrial automation. This paper explores strategies for real-time deadlock detection and recovery, emphasizing the unique challenges imposed by timing requirements and resource dependencies. We examine dynamic detection methods using wait-for graphs, resource allocation matrices, and system monitoring techniques to identify deadlock conditions during execution. Recovery mechanisms, including task preemption, rollback, and resource preemption, are analyzed in terms of their impact on system performance and deadline adherence. The trade-offs between detection accuracy, computational overhead, and recovery feasibility are discussed. The study also reviews recent advancements in predictive and AI-based approaches that enhance real-time responsiveness. Ultimately, effective deadlock management in real-time systems requires a balance between proactive detection and timely, low-cost recovery to maintain system reliability and performance.