End-to-End Timing Analysis of Sporadic Cause-Effect Chains in Distributed Systems

Abstract

A cause-effect chain is used to define the logical order of data dependent tasks, which is independent from the execution order of the jobs of the (periodic/sporadic) tasks. Analyzing the worst-case End-to-End timing behavior, associated to a cause-effect chain, is an important problem in embedded control systems. For example, the detailed timing properties of modern automotive systems are specified in the AUTOSAR Timing Extensions. In this paper, we present a formal End-to-End timing analysis for distributed systems. We consider the two most important End-to-End timing semantics, i.e., the button-to-action delay (termed as the maximum reaction time ) and the worst-case data freshness (termed as the maximum data age ). Our contribution is significant due to the consideration of the sporadic behavior of job activations, whilst the results in the literature have been mostly limited to periodic activations. The proof strategy shows the (previously unexplored) connection between the reaction time (data age, respectively) and immediate forward (backward, respectively) job chains. Our analytical results dominate the state of the art for sporadic task activations in distributed systems and the evaluations show a clear improvement for synthesized task systems as well as for a real world automotive benchmark setting.

Optimal Priority Assignment for Real-Time Systems: A Coevolution-Based Approach

“…To investigate RQ2, we use synthetic subjects in order to freely control key parameters in real-time systems. We create a set of tasks by adopting a wellknown procedure (Emberson et al, 2010) for synthesizing real-time tasks, which has been applied in many schedulability analysis studies (Davis et al, 2008;Zhang and Burns, 2009;Dürr et al, 2019;Grass and Nguyen, 2018;Davis and Burns, 2011). Figure 8 describes a procedure that synthesizes a set of real-time tasks.…”

“…Recall from Section 7.3 that we use the task generation procedure presented in Figure 8 to synthesize tasks. For EXP2, we set some parameter values of the procedure as follows: (1) Target utilization u t = 0.7, which is a common objective in the development of a real-time system in order to guarantee the schedulability of tasks (Fineberg and Serlin, 1967;Dürr et al, 2019). ( 2) The range of task periods [pd min , pd max ] = [10ms, 1s], which are common values in many real-time systems (Emberson et al, 2010;Baruah et al, 2011).…”

AUTOSAR Runnable Periods Optimization for DAG-Based Complex Automobile Applications

“…The above studies commonly assume independent real-time tasks, where there is no data dependency among tasks and each task is responsible for its dedicated control target plant. To deal with practical automobile control applications composed of tasks with complex dependencies, DAG-based control applications had been studied in the context of cause-effect chain analyses of real-time tasks [18][19][20][21]. Even though they are using tasks instead of runnables, their system model is similar to ours.…”

ITANS: Incremental Task and Network Scheduling for Time-Sensitive Networks

“…Especially if tasks from safety-related domains like the aforementioned are related to each other by data dependency in so-called Cause-Effect Chains (CECs) the end-to-end timing behavior becomes more relevant and more difficult to handle. Relevant end-to-end metrics are the end-to-end system response time and the maximum data age [5]. The former expresses the reaction of the system to a change in value [6].…”