{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/sla-violation-prediction-in-cloud-computing-a","title":"SLA Violation Prediction In Cloud Computing: A Machine Learning Perspective","arxiv_id":"1611.10338","date":"2016-11-30","proceeding":null,"authors":["Reyhane Askari Hemmat","Abdelhakim Hafid"],"abstract":"Service level agreement (SLA) is an essential part of cloud systems to ensure\nmaximum availability of services for customers. With a violation of SLA, the\nprovider has to pay penalties. In this paper, we explore two machine learning\nmodels: Naive Bayes and Random Forest Classifiers to predict SLA violations.\nSince SLA violations are a rare event in the real world (~0.2 %), the\nclassification task becomes more challenging. In order to overcome these\nchallenges, we use several re-sampling methods. We find that random forests\nwith SMOTE-ENN re-sampling have the best performance among other methods with\nthe accuracy of 99.88 % and F_1 score of 0.9980.","url_abs":"http://arxiv.org/abs/1611.10338v1","url_pdf":"http://arxiv.org/pdf/1611.10338v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"sla-violation-prediction-in-cloud-computing-a","repo_url":"https://github.com/ReyhaneAskari/SLA_violation_classification","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"cloud-computing","task_name":"Cloud Computing"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}