Abstract
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. Furthermore, 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. 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 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. Hence, Organic Computing concepts like self-X features (self-organization, self-adaptation, self-healing, etc.) are key principles for such systems.
Therefore, the comprehensive adaptive middleware Chameleon has been developed which applies such principles for CPNs. In this paper, the self-adaptation mechanism of Chameleon based on a MAPE-K loop and learning classifier systems is examined and evaluated. The results show its effectivity in autonomously handling the system resources to keep the required constraints of the applications with respect to their criticality.
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Notes
- 1.
Importance must not be confused with priority. Regarding real-time scheduling, components with short periods often result in high priorities (e.g. rate monotonic scheduling). However these components might be less important for the system than components with longer periods and resulting lower priority. In case of overload or lack of resources the lower priority component then has to be preferred.
- 2.
This can be done value based like in classical LCS or precision based (where the best match of measured reward and expected reward are considered) in extended learning classifier systems (XCS).
- 3.
higher values indicate higher Importance and priority.
- 4.
The manually selected adaptation actions have been performed as a proof of concept before the implementation of the autonomous adaptation mechanism was finished.
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Feist, M., Pacher, M., Brinkschulte, U. (2023). Evaluating the Comprehensive Adaptive Chameleon Middleware for Mixed-Critical Cyber-Physical Networks. In: Goumas, G., Tomforde, S., Brehm, J., Wildermann, S., Pionteck, T. (eds) Architecture of Computing Systems. ARCS 2023. Lecture Notes in Computer Science, vol 13949. Springer, Cham. https://doi.org/10.1007/978-3-031-42785-5_14
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