Online-Diagnosis with Organic Computing based on Artificial DNA

Abstract

Organic Computing leads to significant advantages for complex dynamic systems like reduced development efforts, increased adaptability and robustness. However, for safety-critical systems which have to maintain functionality even in the presence of faults or failures (fail-operational) further properties are necessary. This includes the maintenance of the major core functionality even if non-redundant system resources fail, the Organic Computing runtime environment is harmed or the remaining resources are insufficient to maintain all services. These failure scenarios require semantic knowledge of the system combined with fault-diagnosis and adaption techniques to properly degrade and reconfigure the system.This paper highlights the research gaps towards active diagnosis based on artificial DNA and proposes solutions including semantic description methods, optimization algorithms for diagnostic models and adaptation techniques. Semantic description methods for Organic Cmputing systems with artificial DNA are the foundation for higher semantic-based failure detection and adaptation techniques. Diagnosis techniques for Organic Computing systems with artificial DNA can exploit the semantic descriptions to automatically build diagnosis models. Furthermore, these models can be optimized by evolutionary algorithms to improve their failure detection rates. Adaptation techniques modify the artificial DNA based on the recognized failures and the semantic description to realize the reconfiguration and degradation concepts.