Accuracy Improvement of Dataflow Analysis for Cyclic Stream Processing Applications Scheduled by Static Priority Preemptive Schedulers

Stream processing applications executed on embedded multiprocessor systems regularly contain cyclic data dependencies due to the presence of feedback loops and bounded FIFO buffers. Dataflow modeling is suitable for the temporal analysis of such applications. However, the accuracy can be unsatisfactory as existing temporal analysis techniques ignore that cyclic data dependencies limit interference between tasks executed on shared processors. This paper presents a dataflow analysis approach that increases the analysis accuracy by taking into account that cyclic data dependencies limit interference between tasks. It is shown that the approach is applicable for single-rate stream processing applications that are executed on multiprocessor systems using static priority preemptive schedulers. The improvement of accuracy is demonstrated in a case study employing a WLAN 802.11p transceiver application that is executed on a multiprocessor system with shared processors.