🤖 AI Summary
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.
📝 Abstract
Real-time embedded systems require precise timing and fault detection to ensure correct behavior. Traditional tracing tools often rely on local desktops with limited processing and storage capabilities, which hampers large-scale analysis. This paper presents a scalable, cloud-based architecture for software tracing in real-time systems based on microservices and edge computing. Our approach shifts the trace processing workload from the developer's machine to the cloud, using a dedicated tracing component that captures trace data and forwards it to a scalable backend via WebSockets and Apache Kafka. This enables long-term monitoring and collaborative analysis of target executions, e.g., to detect and investigate sporadic errors. We demonstrate how this architecture supports scalable analysis of parallel tracing sessions and lays the foundation for future integration of rule-based testing and runtime verification. The evaluation results show that the architecture can handle many parallel tracing sessions efficiently, although the per-session throughput decreases slightly as the system load increases, while the overall throughput increases. Although the design includes a dedicated tracer for analysis during development, this approach is not limited to such setups. Target systems with network connectivity can stream reduced trace data directly, enabling runtime monitoring in the field.