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• W3006010751 abstract "In order to plan for failure recovery, the designers of cloud systems need to understand how their system can potentially fail. Unfortunately, analyzing the failure behavior of such systems can be very difficult and time-consuming, due to the large volume of events, non-determinism, and reuse of third-party components. To address these issues, we propose a novel approach that joins fault injection with anomaly detection to identify the symptoms of failures. We evaluated the proposed approach in the context of the OpenStack cloud computing platform. We show that our model can significantly improve the accuracy of failure analysis in terms of false positives and negatives, with a low computational cost." @default.
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• W3006010751 date "2019-10-01" @default.
• W3006010751 modified "2023-10-17" @default.
• W3006010751 title "Enhancing Failure Propagation Analysis in Cloud Computing Systems" @default.
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• W3006010751 doi "https://doi.org/10.1109/issre.2019.00023" @default.
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