Characterizing and checking self-healability
Marie-Odile Cordier, Yannick Pencolé, Travé-Massuyès Louise, Vidal Thierry · Frontiers in artificial intelligence and applications · 2008
Real-life complex systems are often required to offer high reliability and quality of service and must be provided with self-management abilities, even in faulty situations. They are expected to be self-aware of their current state and survive autonomously the occurrence of faults, still managing to provide the desired functionality. In other words, such systems must be self-healing [2]. Designing self-healing systems requires to be able to evaluate the joint degree of self-awareness and reactiveness. In the artificial intelligence community, these two properties are better known as diagnosability [3, 1], i.e. the capability of a system to exhibit different observables for different anticipated faulty situations, and repairability, i.e. the ability of a system and its repair actions to cope with any unexpected situation. Checking separately diagnosability and repairability leads to a conservative assessement of self-healability. In this paper, we show that neither standard diagnosability nor repairability of every anticipated fault are necessary to achieve self-healability. Our main contribution consists of defining self-healability as a joint property bridging diagnosability and repairability, which requires a new definition of diagnosability that allows diagnosable subsets of faults to overlap, as opposed to the standard definitions which rely on a partition.