Cost and performance-effective dynamic VM type selection in auto-scaling using reinforcement learning
摘要
Cloud computing enables on-demand resource allocation, but selecting the appropriate resources for efficient auto-scaling remains a significant challenge. Traditional auto-scaling approaches, which rely on a static type of resources to scale strategies, often result in inefficient resource utilization and increased operational costs. One important but often overlooked factor in auto-scaling decisions is the selection of the optimal virtual machine (VM) type, which directly affects the system performance and cost efficiency. This paper presents a dynamic auto-scaling method that optimally selects VM types based on workload demands. By leveraging Reinforcement Learning, our approach identifies the precise resource requirements and dynamically provisions the most suitable VM type to maintain performance while minimizing the overall costs. We conduct extensive evaluations and compare our method with different auto-scaling techniques using real-time workloads. The experimental results show that the proposed auto-scaling method significantly outperforms baseline approaches. The proposed method handles on average 10.49% more process requests, with 7.89