  Service level agreement (SLA) is an essential part of cloud systems to ensure
maximum availability of services for customers. With a violation of SLA, the
provider has to pay penalties. In this paper, we explore two machine learning
models: Naive Bayes and Random Forest Classifiers to predict SLA violations.
Since SLA violations are a rare event in the real world (~0.2 %), the
classification task becomes more challenging. In order to overcome these
challenges, we use several re-sampling methods. We find that random forests
with SMOTE-ENN re-sampling have the best performance among other methods with
the accuracy of 99.88 % and F_1 score of 0.9980.
