The inter-task communication in embedded real-time systems can be achieved using various patterns and be subject to different timing constraints. One of the most basic communication patterns encountered in today’s automotive and aerospace software is the data chain. Each task of the chain reads data from the previous task and delivers the results of its computation to the next task. The data passing does not affect the execution of the tasks that are activated periodically at their own rates. As there is no task synchronization, a task does not wait for its predecessor data; it may execute with old data and get new data at its later release. From the design stage of embedded real-time systems, evaluating if data chains meet their timing requirements, such as the latency constraint, is of the highest importance. The trade-off between accuracy and complexity of the timing analysis is a critical element in the optimization process. In this paper, we consider data chains of real-time periodic tasks executed by a fixed-priority preemptive scheduler upon a single processor. We present a method for the worst-case latency calculation of periodic tasks’ data chains. As the method has an exponential time complexity, we derive a polynomial-time upper bound. Evaluations carried out on an automotive benchmark demonstrate that the average bound overestimation is less than 10 percent of the actual value.

嵌入式实时系统中的任务间通信可以使用各种模式来实现，并且受到不同的时序约束。当今汽车和航空软件中遇到的最基本的通信模式之一就是数据链。链中的每个任务都从上一个任务中读取数据，并将计算结果传递给下一个任务。数据传递不会影响以自己的速度定期激活的任务的执行。由于没有任务同步，因此任务不会等待其前身的数据。它可能会使用旧数据执行，并在以后的版本中获取新数据。从嵌入式实时系统的设计阶段开始，评估数据链是否满足其时序要求（例如延迟约束）至关重要。时序分析的准确性和复杂性之间的权衡是优化过程中的关键要素。在本文中，我们考虑了固定优先级抢占式调度程序在单个处理器上执行的实时周期性任务的数据链。我们提出了一种周期性任务数据链的最坏情况延迟计算方法。由于该方法具有指数时间复杂度，因此我们得出了多项式时间上限。根据汽车基准进行的评估表明，平均界限过高估计小于实际值的10％。我们提出了一种周期性任务数据链的最坏情况延迟计算方法。由于该方法具有指数时间复杂度，因此我们得出了多项式时间上限。根据汽车基准进行的评估表明，平均界限过高估计小于实际值的10％。我们提出了一种周期性任务数据链的最坏情况延迟计算方法。由于该方法具有指数时间复杂度，因此我们得出了多项式时间上限。根据汽车基准进行的评估表明，平均界限过高估计小于实际值的10％。