| _version_ | 1866901715668697088 |
|---|---|
| author | Thafzy V. M. |
| author_facet | Thafzy V. M. |
| contents | <p><span>Efficiently managing resources in cloud computing poses a critical challenge. Over-provisioning inflates costs for both providers and customers, while under-provisioning spikes application latency and risks breaching service level agreements, leading providers to lose customers and revenue. Consequently, researchers are actively pursuing optimal resource management approaches in cloud environments, exploring container place- ment, job scheduling, and multi-resource scheduling. Machine learning plays a pivotal role in these endeavors. This paper offers an extensive survey of machine learning-based solutions for resource management in cloud computing projects, concluding with a comparative analysis of these initiatives. Additionally, it outlines future directions to steer researchers towards further advancements in this domain.</span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_14592545 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | A Review on the Integration of Machine Learning in Cloud Computing Resource Management Thafzy V. M. computing, resource manage- ment, supervised learning, unsupervised learning, semisupervised learning, machine learning. <p><span>Efficiently managing resources in cloud computing poses a critical challenge. Over-provisioning inflates costs for both providers and customers, while under-provisioning spikes application latency and risks breaching service level agreements, leading providers to lose customers and revenue. Consequently, researchers are actively pursuing optimal resource management approaches in cloud environments, exploring container place- ment, job scheduling, and multi-resource scheduling. Machine learning plays a pivotal role in these endeavors. This paper offers an extensive survey of machine learning-based solutions for resource management in cloud computing projects, concluding with a comparative analysis of these initiatives. Additionally, it outlines future directions to steer researchers towards further advancements in this domain.</span></p> |
| title | A Review on the Integration of Machine Learning in Cloud Computing Resource Management |
| topic | computing, resource manage- ment, supervised learning, unsupervised learning, semisupervised learning, machine learning. |
| url | https://doi.org/10.5281/zenodo.14592545 |