Real-time stream processing applications, such as radios, can often be modeled intuitively with dataflow models. Given the Worst-Case Execution Times (WCETs) of the tasks, which characterizes workload with one parameter, dataflow analysis techniques have been used to compute the minimum throughput and maximum latency of these applications. However, a large difference between the WCETs of the tasks and their average execution times can result in a large difference between the computed worst-case throughput and the actual obtained throughput.
To reduce the difference between the worst-case throughput, determined by analysis, and the actual obtained throughput, we introduce in this paper a two parameter (σ,Ͽ) workload characterization of the tasks to improve the accuracy of dataflow analysis. The (σ, Ͽ) workload characterization captures information on the maximum cumulative execution time of consecutive executions of a task and can therefore be seen as a generalization of the WCET characterization.
We show how the (σ,Ͽ) workload characterization can be used in combination with several types of dataflow graphs and how it can be used to improve the temporal analysis results of real-time stream processing applications. We illustrate this for a DVB-T radio application, a car-radio application and a data-dependent MP3 playback application.
|Publisher||IEEE Computer Society|
|Conference||19th Real-Time and Embedded Technology and Applications Symposium (RTAS)|
|Period||9/04/13 → 11/04/13|
|Other||9-11 April 2013|