Reaction Time Analysis of Event-Triggered Processing Chains with Data Refreshing

Many real-time systems process and react to external events by a chain of tasks, and have constraints on the maximum reaction time which describes how long it takes to respond to an external event. While a processing chain typically starts with a sampling task periodically triggered to sample the sensor data, other tasks in the chain could be triggered in two different ways: event-triggered or time-triggered, which have their own pros and cons. In this paper, we propose the third option to trigger the processing tasks in a chain, namely, the event-triggered with data refreshing approach, which combines the benefits of the event-triggered or time-triggered approaches. As the main technical contribution, we develop techniques to formally upper-bound its maximum reaction time and analytically compare it with the existing approaches. Experiments with synthetic workload are conducted to show the performance improvement by our proposed techniques.