Over the years, schedulability of Cyber-Physical Systems (CPS) has mainly been performed by analytical methods. These techniques are known to be effective but limited to a few classes of scheduling policies. In a series of recent work, we have shown that schedulability analysis of CPS could be performed with a model-based approach and extensions of verification tools such as UPPAAL. One of our main contributions has been to show that such models are flexible enough to embed various types of scheduling policies, which goes beyond those in the scope of analytical tools.However, the specification of scheduling problems with model-based approaches requires a substantial modeling effort, and a deep understanding of the techniques employed in order to understand their results. In this paper we propose simplicity-driven high-level specification and verification frameworks for various scheduling problems. These frameworks consist of graphical and user-friendly languages for describing scheduling problems. The high-level specifications are then automatically translated to formal models, and results are trans- formed back into the comprehensible model view. To construct these frameworks we exploit a meta-modeling approach based on the tool generator Cinco.Additionally we propose in this paper two new techniques for scheduling analysis. The first performs runtime monitoring using the CUSUM algorithm to detect alarming change in the system. The second performs optimization using efficient statistical techniques. We illustrate our frameworks and techniques on two case studies.
Abstract. Over the years, schedulability of Cyber-Physical Systems (CPS) has mainly been performed by analytical methods. Those techniques are known to be effective but limited to a few classes of scheduling policies. In a series of recent work, we have shown that schedulability analysis of CPS could be performed with a model-based approach and extensions of verification tools such as UPPAAL. One of our main contribution has been to show that such models are flexible enough to embed various types of scheduling policies that go beyond those in the scope of analytical tools. In this paper, we go one step further and show how our formalism can be extended to account for stochastic information, such as sporadic tasks whose attributes depend on the hardware domain.
High-security processes have to load confidential information into shared resources as part of their operation. This confidential information may be leaked (directly or indirectly) to low-security processes via the shared resource. This paper considers leakage from high-security to low-security processes from the perspective of scheduling. The workflow model is here extended to support preemption, security levels, and leakage. Formalization of leakage properties is then built upon this extended model, allowing formal reasoning about the security of schedulers. Several heuristics are presented in the form of compositional preprocessors and postprocessors as part of a more general scheduling approach. The effectiveness of such heuristics are evaluated experimentally, showing them to achieve significantly better schedulability than the state of the art. Modeling of leakage from cache attacks is presented as a case study.
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