Abstract
With the large scale and user demands on modern cloud systems there is a need for autonomous approaches to self-healing. When there is no operator in the loop for self-healing actions, it is crucial to ensure that the actions taken are safe and effective. In this paper we propose SASH: Safe Autonomous Self-Healing, which uses surrogate models to estimate the safety and effectiveness of self-healing actions. SASH uses system metrics, configuration parameters, domain information and available actions to decide on the best fault remediation action or combination of actions. The performance of the action(s) are then verified through a validation block that updates the knowledge base with how the actions performed for that fault. This data is then used to update the safety and effectiveness estimation algorithm. The results show the framework is able to successfully remediate faults with a low number of actions and with protection against unsafe actions.
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White, G., Custode, L.L., O’Brien, O. (2023). SASH: Safe Autonomous Self-Healing. In: Troya, J., et al. Service-Oriented Computing – ICSOC 2022 Workshops. ICSOC 2022. Lecture Notes in Computer Science, vol 13821. Springer, Cham. https://doi.org/10.1007/978-3-031-26507-5_12
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