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Safety-critical embedded systems such as autonomous vehicles typically have only very limited computational capabilities on board that must be carefully managed to provide required enhanced functionalities. As these systems become more complex and inter-connected, some parts may need to be secured to prevent unauthorized access, or isolated to ensure correctness. We propose the multi-phase secure (MPS) task model as a natural extension of the widely used sporadic task model for modeling both the timing and the security (and isolation) requirements for such systems. Under MPS, task phases reflect execution using different security mechanisms which each have associated execution time costs for startup and teardown. We develop corresponding limited-preemption EDF scheduling algorithms and associated pseudo-polynomial schedulability tests for constrained-deadline MPS tasks. In doing so, we provide a correction to a long-standing schedulability condition for EDF under limited-preemption. Evaluation shows that the proposed tests are efficient to compute for bounded utilizations. We empirically demonstrate that the MPS model successfully schedules more task sets compared to non-preemptive approaches. 

Multi-axial real-time hybrid simulation (maRTHS) uses multiple hydraulic actuators to apply loads and deform experimental substructures, enacting bothtranslationalandrotationalmotion. This allows for an increased level of realism in seismic testing. However, this also demands the implementation of multiple-input, multiple-output control strategies with complex nonlinear behaviors. To realize true real-time hybrid simulation at the necessary sub-millisecond timescales, computational platforms will need to support these complexities at scale, while still providing deadline assurance. This paper presents initial work towards supporting (and is influenced by the need for) envisioned larger-scale future experiments based on the current maRTHS benchmark: it discusses aspects of hardware, operating system kernels, runtime middleware, and scheduling theory that may be leveraged or developed to meet those goals. This work aims to create new concurrency platforms capable of managing task scheduling and adaptive event handling for computationally intensive numerical simulation and control models like those for the maRTHS benchmark problem. These should support real-time behavior at millisecond timescales, even for large complex structures with thousands of degrees of freedom. Temporal guarantees should be maintained across behavioral and computational mode changes, e.g., linear to nonlinear control. Pursuant to this goal, preliminary scalability analysis is conducted towards designing future maRTHS experiments. The results demonstrate that the increased capabilities of modern hardware architectures are able to handle larger finite element models compared to prior work, while imposing the same latency constraints. However, the results also illustrate a subtle challenge: with larger numbers of CPU cores, thread coordination incurs more overhead. These results provide insight into the computational requirements to support envisioned future experiments that will take the maRTHS benchmark problem to nine stories and beyond in scale. In particular, this paper (1) re-evaluates scalability of prior work on current platform hardware, and (2) assesses the resource demands of a basic smaller scale model from which to gauge the projected scalability of the new maRTHS benchmark as ever larger and more complex models are integrated within it.