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"Cloud failure prediction based on traditional machine learning and dee" by Tengku Nazmi Tengku Asmawi, Azlan Ismail et al.

Cloud failure is one of the critical issues since it can cost millions of dollars to cloud service providers, in addition to the loss of productivity suffered by industrial users. Fault tolerance management is the key approach to address this issue, and failure prediction is one of the techniques to prevent the occurrence of a failure. One of the main challenges in performing failure prediction is to produce a highly accurate predictive model. Although some work on failure prediction models has been proposed, there is still a lack of a comprehensive evaluation of models based on different types of machine learning algorithms. Therefore, in this paper, we propose a comprehensive comparison and model evaluation for predictive models for job and task failure. These models are built and trained using five traditional machine learning algorithms and three variants of deep learning algorithms. We use a benchmark dataset, called Google Cloud Traces, for training and testing the models. We eva ....

Google Cloud Traces , Extreme Gradient Boosting , Decision Tree , Random Forest , Logistic Regression , Cloud Computing , Deep Learning , Failure Prediction , Ob And Task Failure , Machine Learning ,