深度学习在主机分布式集群负载均衡中的技术应用  

The Technical Application of Deep Learning in Load Balancing of Host Distributed Clusters

作　　者：唐春娜 TANG Chunna(Guangdong Baiyun University,Guangzhou Guangdong 510000,China)

机构地区：[1]广东白云学院,广东广州510000

出　　处：《信息与电脑》2024年第17期59-61,共3页Information & Computer

摘　　要：本文探讨了深度学习技术在主机分布式集群负载均衡中的应用。通过对深度学习算法的分析,结合实际的主机集群环境,本文提出了一种基于深度学习的负载均衡策略。该策略通过对主机历史负载数据的学习,可预测未来一段时间内的负载情况,动态调整负载均衡策略,以实现集群负载的实时优化。实验结果表明,相比传统的负载均衡算法,该策略在吞吐量、响应时间等指标上都有明显的提升,证明了深度学习技术在负载均衡领域的应用价值。This paper discusses the application of deep learning technology in host distributed cluster load balancing.Based on the analysis of deep learning algorithm and combining with the actual host cluster environment,a load balancing strategy based on deep learning is proposed.By learning the historical load data of the host,the strategy predicts the load situation in the future period of time,dynamically adjusts the load balancing strategy,and realizes the real-time optimization of the cluster load.The experimental results show that compared with the traditional load balancing algorithm,this strategy has significantly improved in the throughput,response time and other indicators,which proves the application value of deep learning technology in the field of load balancing.

关 键 词：深度学习 主机集群 负载均衡 智能调度 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]