大模型驱动下的云资源动态分配策略研究  

Research on dynamic allocation strategy of cloud resource driven by large model

作　　者：李晨 陈炳宇 赵杰鹏 齐海乐 LI Chen;CHEN Bingyu;ZHAO Jiepeng;QI Haile(Beijing Smart Net Digital Technology Co.,Ltd.,Beijing 100020,China)

机构地区：[1]北京智网数科技术有限公司,北京100020

出　　处：《计算机应用文摘》2024年第23期148-150,共3页

摘　　要：随着云计算和人工智能技术的快速发展,大模型(如深度学习模型)在各类场景中得到了广泛应用,促使云资源的动态分配策略成了研究热点。文章探讨了大模型驱动下的云资源动态分配策略,分析了现有方法的局限性,并提出一种基于机器学习的智能动态分配框架,旨在提高资源利用效率,加快响应速度。通过实验验证,该框架在实际应用中表现出更优的资源调度效果和较低的延迟。With the rapid development of cloud computing and artificial intelligence technology,large models(such as deep learning models)have been widely used in various scenarios,which makes the dynamic allocation strategy of cloud resources become a research hotspot.This paper discusses the dynamic allocation strategy of cloud resources driven by large models,analyzes the limitations of existing methods,and proposes an intelligent dynamic allocation framework based on machine learning to improve resource utilization efficiency and speed up response.The experimental results show that the framework has better resource scheduling effect and lower delay in practical application.

关 键 词：云计算 大模型 资源分配 动态调度 机器学习 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]