Monitoring the performance of multicore embedded systems is crucial to properly ensure their timing requirements. Collecting performance data is also very relevant for optimization and validation efforts. However, the strategies used to monitor and capture data in such systems are complex to design and implement since they must not interfere with the running system beyond the point at which the system's timing and performance characteristics start to get affected by the monitoring strategies. In this paper, we extend a monitoring framework developed in previous work to encompass three monitoring strategies, namely Active and Passive Periodic monitoring and Job-based monitoring. Periodic monitoring follows a given sampling rate. Active Periodic relies on periodic timer interrupts to guarantee deterministic sampling, while Passive Periodic trades determinism for a less invasive strategy, sampling data onlywhen ordinary system events are handled. Job-based follows an event-driven monitoring that samples data whenever a job leaves the CPU, thus building isolated traces for each job. We evaluate them according to overhead, latency, and jitter, where none of them presented an average impact on the system execution time higher than 0.3%. Moreover, a qualitative analysis is conducted in terms of data quality. On one hand, while Periodic monitoring allows for configurable sampling rates, it does not account for the rescheduling of jobs and may capture mixed traces. On the other hand, Job-based monitoring provides data samples tied to the execution of each job while disregarding sampling rate configuration and may lose track of instant measures.
