Analysis of Job Failure Prediction in a Cloud Environment by Applying Machine Learning Techniques

Faraz Bashir ()
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Faraz Bashir: Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan

International Journal of Innovations in Science & Technology, 2022, vol. 4, issue 5, 148-156

Abstract: Cloud services are the on-demand availability of resources like storage, data, and computing power. Nowadays, cloud computing and storage systems are continuing to expand; there is an imperative requirement for CSPs(Cloud Service providers) to ensure a reliable and consistent supply of resources to users and businesses in case of any failure. Consequently, large cloud service providers are concentrating on mitigating any losses in a cloud system environment. In this research, we examined the bit brains dataset for job failure prediction, which keeps traces of 3 years of cloud system VMs. The dataset contains data about the resources used in a cloud environment. We proposed the performance of two machine learning algorithms: Logistic-Regression and KNN. The performance of these ML algorithms has been assessed using cross-validation. KNN and Logistic Regression give optimal results with an accuracy of 99% and 95%. Our research shows that using KNN and Logistic Regression increases the detection accuracy of job failures and will relieve cloud-service providers from diminishing future losses in cloud resources. Thus, we believe our approach is feasible and can be transformed to apply in an existing cloud environment.

Keywords: Cloud Service Providers; Virtual Machines; Physical Machines; Machine Learning; Infrastructure as a Service (search for similar items in EconPapers)
Date: 2022
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