Handling Out-Of-Order Events with Event Time Consistency Models

Out-of-order events occur in real-time distributed systems when event arrivals deviate from the intended sequence due to network delays, clock skews, or processing variations. Handling these events efficiently is crucial for ensuring accurate event-time consistency, particularly in Internet of Things (IoT) analytics, financial transactions, and real-time monitoring systems. Traditional approaches rely on fixed watermarking and buffering techniques, which often lead to increased latency, inaccurate aggregations, and excessive memory consumption. This study employs an Adaptive Event-Time Consistency model, which integrates Dynamic Watermark Adaptation, Predictive Event Ordering, and Memory-Efficient Late Event Handling. The proposed method adjusts watermarks in real time, leverages machine learning (XGBoost) for delay estimation and reordering, and optimizes memory allocation through a tiered buffering strategy. The experimental evaluation demonstrates a $\mathbf{4 0 \%}$ reduction in latency, $\mathbf{1 2 - 1 8 \%}$ improvement in aggregation accuracy, and $\mathbf{4 0 \%}$ optimization in memory utilization. These findings confirm that the proposed model significantly enhances real-time event consistency, making it highly scalable and efficient for streaming applications. The method ensures low latency, accurate time-based aggregations, and optimized resource management, addressing the critical challenges of out-of-order event handling in modern distributed systems.