Evaluating the Comprehensive Adaptive Chameleon Middleware for Mixed-Critical Cyber-Physical Networks

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.