Offloading tracing for real-time systems using a scalable cloud infrastructure
Traditional local tracing tools face limitations in processing capability, real-time performance, and scalability. To address these challenges, this paper proposes a distributed, cloud-native tracing architecture leveraging microservices and edge computing. The architecture employs WebSocket for low-latency data ingestion and Apache Kafka for high-throughput, reliable transmission of embedded-system tracing data, enabling concurrent multi-session processing, lightweight streaming upload, and collaborative cloud-based analysis. Innovatively integrating edge-side preprocessing with cloud-side elastic scaling, it simultaneously supports rule-based validation during development and runtime fault diagnosis. Experimental results demonstrate that the system achieves linear throughput scaling with increasing load while maintaining stable per-session throughput. It effectively enables large-scale, long-term monitoring and collaborative debugging of intermittent faults. Consequently, the architecture significantly enhances observability and maintainability of real-time embedded systems.