Efficient scaling with machine learning on cloud environment

Scaling in cloud environments can significantly affect the cost and efficiency of a system. In order to properly plan capacity, calculations are often made based on scaling size. Applications typically reserve resources such as cores, memory, and network in order to maintain high quality of service (QoS). When resource limits are approached, the application is scaled by running multiple copies in order to ensure system reliability under increasing traffic. This study aims to more efficiently reserve resources in order to maintain high QoS through the use of machine learning. Traditional methods reserve resources for an application and scale the application when resource usage reaches a certain threshold. However, the method implemented in this study estimates incoming traffic and scales applications based on this estimation, resulting in more efficient performance compared to using a fixed threshold value. The results of the study showed that the scaling system created based on machine learning performs more efficiently in terms of cost and resource allocation compared to the currently used static scaling methods. Keywords: Machine Learning, Cloud Computing, Scaling, Kubernetes, Resource Management, Cloud Cost