Symbolic Reliability Analysis of Self-healing Networked Embedded Systems

In recent years, several network online algorithms have been studied that exhibit self-x properties such as self-healing or self-adaption. These properties are used to improve systems characteristics like, e.g., fault-tolerance, reliability, or load-balancing. In this paper, a symbolic reliability analysis of self-healing networked embedded systems that rely on self-reconfiguration and self-routing is presented. The proposed analysis technique respects resource constraints such as the maximum computational load or the maximum memory size, and calculates the achievable reliability of a given system. This analytical approach considers the topology of the system, the properties of the resources, and the executed applications. Moreover, it is independent of the used online algorithms that implement the selfhealing properties, but determines the achievable upper bound for the systems reliability. Since this analysis is not tailored to a specific online algorithm, it allows a reasonable decision making on the used algorithm by enabling a rating of different self-healing strategies. Experimental results show the effectiveness of the introduced technique even for large networked embedded systems.