# SLA Violation Prediction In Cloud Computing: A Machine Learning Perspective

arXiv: https://arxiv.org/abs/1611.10338 · HTML: https://mlsanity.com/paper/1611.10338/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2016.

## Built on by (most cited first, 2 of 2)

- [Towards a consistent interpretation of AIOps models](https://mlsanity.com/paper/2202.02298/) (2022)
- [A Stochastic Analysis of the Linguistic Provenance of English Place Names](https://mlsanity.com/paper/2312.12850/) (2023)

## Abstract (arXiv)

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.