Persistent observability under memory and storage constraints in embedded Linux platforms
Keywords:
embedded Linux, system observability, persistent logging, resource constrained systems, reliability monitoringAbstract
Embedded Linux platforms used in augmented reality and wearable devices require persistent system observability for failure diagnosis and reliability monitoring. However, continuous logging can introduce significant memory overhead and storage wear in resource-constrained environments. This study investigates persistent observability mechanisms under memory and storage constraints in embedded Linux systems. A lightweight observability architecture is proposed that combines severity-aware log buffering, adaptive persistence scheduling, and memory-efficient event summarization. The framework was implemented at both kernel and user-space levels and evaluated on an ARM-based embedded Linux platform running XR workloads. Experiments were conducted over 120 hours of system operation, generating approximately 28 million runtime log entries under mixed workloads including sensor processing, rendering tasks, and network communication. Compared with conventional persistent logging approaches, the proposed method reduces storage write volume by 36.8%, lowers memory overhead by 29.4%, and maintains 97.2% retention of critical diagnostic events during simulated crash scenarios. In stress-testing experiments involving 520 injected failure events, the architecture maintained reliable observability while reducing I/O latency by 22%. These results indicate that adaptive persistence strategies can significantly improve observability efficiency in embedded Linux platforms.
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Copyright (c) 2026 Daniel J. Thompson, Emily R. Chen, Michael A. Brown
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