Combining Offsets with Precedence Constraints to Improve Temporal Analysis of Cyclic Real-Time Streaming Applications

Stream processing applications executed on multiprocessor systems usually contain cyclic data dependencies due to the presence of bounded FIFO buffers and feedback loops, as well as cyclic resource dependencies due to the usage of shared processors. In recent works it has been shown that temporal analysis of such applications can be performed by iterative fixed-point algorithms that combine dataflow and response time analysis techniques. However, these algorithms consider resource dependencies based on the assumption that tasks on shared processors are enabled simultaneously, resulting in a significant overestimation of interference between such tasks. This paper extends these approaches by integrating an explicit consideration of precedence constraints with a notion of offsets between tasks on shared processors, leading to a significant improvement of temporal analysis results for cyclic stream processing applications. Moreover, the addition of an iterative buffer sizing enables an improvement of temporal analysis results for acyclic applications as well. The performance of the presented approach is evaluated in a case study using a WLAN transceiver application. It is shown that 56% higher throughput guarantees and 52% smaller end-to-end latencies can be determined compared to state-of-the-art.