Modeling runtime state drift in long duration augmented reality sessions
DOI:
https://doi.org/10.66238/jaeee21Keywords:
augmented reality systems, runtime drift modeling, system observability, performance degradation, log analyticsAbstract
Long-duration augmented reality (AR) applications often exhibit gradual runtime state drift caused by cumulative resource contention, asynchronous service interactions, and sensor–rendering pipeline imbalance. Such drift can significantly degrade system responsiveness and lead to intermittent failures that are difficult to diagnose through conventional log inspection. This study proposes a runtime state drift modeling framework that combines temporal log embedding, system telemetry correlation, and drift-aware sequence modeling to characterize performance degradation in extended AR sessions. The framework constructs joint representations from system logs, GPU utilization traces, memory allocation records, and frame latency metrics. Experiments were conducted using a dataset containing 92 hours of AR runtime traces from 138 testing sessions, generating over 64 million log events. The proposed method identifies drift patterns using a hybrid Transformer–LSTM architecture with temporal attention. Results show that the approach improves drift detection accuracy to 91.3%, outperforming statistical baseline models by 17.6%, while reducing average diagnostic latency by 34%. Furthermore, correlation analysis reveals that rendering pipeline synchronization and memory fragmentation account for over 46% of long-session performance degradation events. The proposed modeling approach provides an effective tool for understanding runtime degradation mechanisms and improving system observability in AR platforms.
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