Using reinforcement learning algorithms to dynamically allocate computing resources in cloud environments

Abstract

Finding the method to most efficiently allocate resources in cloud computing environments has long been a challenge facing the cloud computing field, necessitating unique strategies to optimize performance while minimizing costs. The dynamic nature of cloud computing environments paired with users’ desire for cost-effective solutions requires intelligent and adaptive resource allocation methods. In this paper, we investigate the possibility of utilizing reinforcement learning (RL) algorithms to effectively handle this resource allocation problem. The findings provide insight into the possibility of using RL algorithms for cloud resource management and provide a base for further research and exploration in the area.