Tasks executing on general purpose multiprocessor platforms exhibit variations in their execution times. As such, there is a need to explicitly consider robustness, i.e., tolerance to these fluctuations. This work aims to quantify the robustness of schedules of directed acyclic graphs (DAGs) on multiprocessors by defining probabilistic robustness metrics and to present a new approach to perform robustness analysis to obtain these metrics. Stochastic execution times of tasks are used to compute completion time distributions which are then used to compute the metrics. To overcome the difficulties involved with the max operation on distributions, a new curve fitting approach is presented using which we can derive a distribution from a combination of analytical and limited simulation based results. The approach has been validated on schedules of time-critical applications in ASML wafer scanners.

Abstract-Latest trends in embedded platform architectures show a steady shift from high frequency single core platforms to lower-frequency but highly-parallel execution platforms. Scheduling applications with stringent latency requirements on such multiprocessor platforms is challenging. Our work is motivated by the scheduling challenges faced by ASML, the world's leading provider of wafer scanners. A wafer scanner is a complex cyber-physical system that manipulates silicon wafers with extreme accuracy at high throughput. Typical control applications of the wafer scanner consist of thousands of precedence-constrained tasks with latency requirements. Machines are customized so that precise characteristics of the control applications to be scheduled and the execution platform are only known during machine start-up. This results in large-scale scheduling problems that need to be solved during start-up of the machine under a strict timing constraint on the schedule delivery time. This paper introduces a fast and scalable static-order scheduling approach for applications with stringent latency requirements and a fixed binding on multiprocessor platforms. It uses a heuristic that makes scheduling decisions based on a new metric to find feasible schedules that meet timing requirements as quickly as possible and it is shown to be scalable to very large task graphs. The computation of this metric exploits the binding information of the application. The approach will be incorporated into the ASML's latest generation of wafer scanners.

Cyber-Physical Systems (CPS) play an important role in the modern high-tech industry. Designing such systems is an especially challenging task due to the multi-disciplinary nature of these systems, and the range of abstraction levels involved. To facilitate hands-on experience with such systems, we develop a cyber-physical platform that aids in both research and education on CPS. This paper describes this platform, which contains all typical CPS components. The platform is used in various research and education projects for bachelor, master, and PhD students. We discuss the platform and illustrate its use with a number of projects and the educational opportunities they provide.

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Cyber-Physical Systems (CPS) play an important role in the modern high-tech industry. Designing such systems is a challenging task due to the multi-disciplinary nature of these systems, and the range of abstraction levels involved. To facilitate hands-on experience with such systems, we develop a cyber-physical platform that aids in research and education on CPS. This paper describes this platform, which contains all typical CPS components. The platform is used in various research and education projects for bachelor, master, and PhD students. We discuss the platform and a number of projects and the educational opportunities they provide.

Abstract-Analyzing worst-case application timing for systems with shared resources is difficult, especially when non-monotonic arbitration policies like First-Come-First-Served (FCFS) scheduling are used in combination with varying task execution times. Analysis methods that conservatively analyze these systems are often based on state-space exploration, which is not scalable due to its inherent susceptibility to combinatorial explosion.We propose a scalable timing analysis method on periodically restarted Directed Acyclic Task Graphs, that can provide conservative bounds on task timing properties when shared resources with FCFS scheduling are used. By expressing task enabling and completion times in intervals, denoting best-case and worst-case timing properties, contention on the shared resources can be estimated using conservative approximations.With an industrial case study we show that our approach can easily analyze models with thousands of tasks in less than 10 seconds, and the worst-case bounds obtained show an average improvement of 46% compared to bounds obtained by static worst-case analysis.

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