Temporal Analysis of Static Priority Preemptive Scheduled Cyclic Streaming Applications using CSDF Models

Real-time streaming applications with cyclic data dependencies that are executed on multiprocessor systems with processor sharing usually require a temporal analysis to give guarantees on their temporal behavior at design time. Current accurate analysis techniques for cyclic applications that are scheduled with Static Priority Preemptive (SPP) schedulers are however limited to the analysis of applications that can be expressed with Homogeneous Synchronous Dataflow (HSDF) models, i.e. in which all tasks operate at a single rate. Moreover, it is required that both input and output buffers synchronize atomically at the beginnings and finishes of task executions, which is difficult to realize on many existing hardware platforms.\ud \ud This paper presents a temporal analysis approach for cyclic real-time streaming applications executed on multiprocessor systems with processor sharing and SPP scheduling that can be expressed using Cyclo-Static Dataflow (CSDF) models. This allows to model tasks with multiple phases and changing rates and furthermore resolves the problematic restriction that buffer synchronization must occur atomically at the boundaries of task executions. For that purpose a joint interference characterization over multiple phases is introduced, which realizes a significant accuracy improvement compared to an isolated consideration of interference.\ud \ud Applicability, efficiency and accuracy of the presented approach are evaluated in a case study using a WLAN 802.11p transceiver application. Thereby different use-cases of CSDF modeling are discussed, including a CSDF model relaxing the requirement of atomic synchronization

Appendix to Temporal analysis of static priority preemptive scheduled cyclic streaming applications using CSDF models

This is the appendix to the paper Temporal Analysis of Static Priority Preemptive Scheduled Cyclic Streaming Applications using CSDF Models [1]. The temporal analysis approach presented in [1] makes use of an iterative algorithm that computes so-called maximum busy periods over multiple task phases. The algorithm contains a stop criterion indicating after which iteration of the algorithm subsequent iterations do not need to be considered. The intuition behind that stop criterion is given in the paper and supplemented by a formal proof in this appendix