基于强化学习的大数据分析服务自动扩展方法

打开文本图片集

中图分类号：TP18 文献标识码：A 文章编号：2096-4706（2025）09-0032-06

Abstract： As the demand for big data analytics services continues to grow， AaaS （Analysis as a Service） providers often rely on leasing IaaS （Infrastructure as a Service） virtual machine resources to build their service platforms. However， the dynamic changes in user requirements make how to flexibly adjust the type and number of virtual machines a key issue that needs to be resolved. In this paper， an automatic expansion method based on Q-Learning is proposed. By using the number of virtual machine instances as the action space of learning， the ε-greedy strategy is used to select actions， and the status and Q table values are dynamically updated. Through repeated iterations of this process， this method can maximize the utilization of virtual machine resources while minimizing the cost of AaaS providers. The experimental results show that this method can not only effectively reduce operating costs， but also ensure that the virtual machine CPU maintains high utilization for a long time.

Keywords： Big Data analysis; reinforcement learning; Artificial Intelligence

0 引 言

通过大数据分析，用户可以从数据中获取有价值的信息，从而做出实时且准确的决策 [1]。（剩余8050字）