GoodSpread: Criticality-Aware Static Scheduling of CPS with Multi-QoS Resources

Roy, Debayan; Ghosh, Sumana; Zhu, Qi; Caccamo, Marco; Chakraborty, Samarjit

doi:10.1109/rtss49844.2020.00026

Cited by 9 publications

(1 citation statement)

References 31 publications

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“…GoodSpread is a framework that statically allocates multi QoS resources to a set of control applications so that each of them meets its performance requirements in all scenarios while using the minimum amount of high-QoS resources [Roy+20]. All of those approaches address mixed-criticality in terms of scheduling only.…”

Section: Handling Mixed-criticalitymentioning

confidence: 99%

Comprehensive adaptive middleware for mixed-critical cyber-physical networks

Feist

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Cyber Physical Systems (CPS) are growing more and more complex due to the availability of cheap hardware, sensors, actuators and communication links. A network of cooperating CPSs (CPN) additionally increases the complexity. This poses challenges as well as it offers chances: the increasing complexity makes it harder to design, operate, optimize and maintain such CPNs. However, on the other side an appropriate use of the increasing resources in computational nodes, sensors, actuators can significantly improve the system performance, reliability and flexibility. Therefore, self-X features like self-organization, self-adaptation and self-healing are key principles for such systems. Additionally, CPNs are often deployed in dynamic, unpredictable environments and safety-critical domains, such as transportation, energy, and healthcare. In such domains, usually applications of different criticality level exist. In an automotive environment for example, the brake has a higher criticality level regarding safety as the infotainment. As a result of mixed-criticality, applications requiring hard real-time guarantees compete with those requiring soft real-time guarantees and best-effort application for the given resources within the overall system. This leads to the need to accommodate multiple levels of criticality while ensuring safety and reliability, which increases the already high complexity even more. This thesis deals with the question on how to conveniently, effectively and efficiently handle the management and complexity of mixed-critical CPNs (MC-CPNs). Since this cannot be done by the system developer without the assistance of the system itself any longer, it is essential to develop new approaches and techniques to ensure that such systems can operate under a range of conditions while meeting stringent requirements. Based on five research hypothesis, this thesis introduces a comprehensive adaptive mixed-criticality supporting middleware for Cyber-Physical Networks (Chameleon), which efficiently and autonomously takes care of the management and complexity of CPNs with regard to the mixed-criticality aspect. Chameleon contributes to the state-of-art by introducing and combining the following concepts: - A comprehensive self-adaption mechanism on all levels of the system model is provided. - This mechanism allows a flexible combination of parametric and structural adaptation actions (relocation, scheduling, tuning, ...) to modify the behavior of the system. - Real-time constraints of mixed-critical applications (hard real-time, soft real-time, best-effort) are considered in all possible adaptation conditions and actions by the use of the importance parameter. - CPNs are supported by the introduction of different scopes (local, system, global) for the adaptation conditions and actions. This also enables the combination of different scopes for conditions and actions. - The realization of the adaptation with a MAPE-K loop instantiated by a distributed LCS allows for real-time capable reasoning of adaptation actions which also works on resource-spare systems. - The developed rule language Rango offers an intuitive way to specify an initial rule set for LCS in the context of CPS/CPNs and supports the system administrators in the process of rule set generation.

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Section: Handling Mixed-criticalitymentioning

confidence: 99%