AI Bibliography
AI Bibliography |
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Ghahremani, S., & Giese, H. (2020). Evaluation of self-healing systems: An analysis of the state-of-the-art and required improvements. Computers, 9(1), 16. |
| Resource type: Journal Article BibTeX citation key: Ghahremani2020 View all bibliographic details  |
Categories: Artificial Intelligence, Cognitive Science, Computer Science, Data Sciences, Decision Theory, Engineering, General, Military Science Subcategories: Autonomous systems, Big data, Decision making, Edge AI, Fog computing, Machine learning, Networked forces Creators: Ghahremani, Giese Publisher: Collection: Computers |
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| Abstract |
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Evaluating the performance of self-adaptive systems is challenging due to their interactions with often highly dynamic environments. In the specific case of self-healing systems, the performance evaluations of self-healing approaches and their parameter tuning rely on the considered characteristics of failure occurrences and the resulting interactions with the self-healing actions. In this paper, we first study the state-of-the-art for evaluating the performances of self-healing systems by means of a systematic literature review. We provide a classification of different input types for such systems and analyse the limitations of each input type. A main finding is that the employed inputs are often not sophisticated regarding the considered characteristics for failure occurrences. To further study the impact of the identified limitations, we present experiments demonstrating that wrong assumptions regarding the characteristics of the failure occurrences can result in large performance prediction errors, disadvantageous design-time decisions concerning the selection of alternative self-healing approaches, and disadvantageous deployment-time decisions concerning parameter tuning. Furthermore, the experiments indicate that employing multiple alternative input characteristics can help with reducing the risk of premature disadvantageous design-time decisions.
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