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计算机系统应用:2018,27(9):220-223
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基于预测模型及独立训练节点的负载均衡策略
陈大才1,2, 吕立2, 高岑2, 孙咏2
(1.中国科学院大学, 北京 100049;2.中国科学院 沈阳计算技术研究所, 沈阳 110168)
Load Balancing Strategy Based on Predictive Model and Independent Training Nodes
CHEN Da-Cai1,2, LYU Li2, GAO Cen2, SUN Yong2
(1.University of Chinese Academy of Sciences, Beijing 100049, China;2.Shenyang Institute of Computer Technology, Chinese Academy of Sciences, Shenyang 110168, China)
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本文已被:浏览 171次   下载 176
投稿时间:2018-01-07    修订日期:2018-02-09
中文摘要: 随着业务量、用户量的增大,提高服务器集群的效率变得越来越重要.本文使用机器学习算法,通过对历史数据进行训练得到响应时间预测模型,来预测新请求的响应时间,根据每个服务器节点的预估响应时间将请求分配给具有最少响应时间的服务器节点,从而提高集群中请求分配的均衡性,提高集群的效率.本文通过对三种机器学习算法的实验,均表明本策略能降低小集群高并发场景中系统的平均响应时间.
Abstract:As business and users increase, it becomes more and more important to improve the efficiency of server clusters. In this study, the machine learning algorithm is used to predict the response time of new requests by training the historical data. According to the estimated response time of each server node, the request is allocated to the server node with the least response time. The balanced allocation of requests in a cluster has been improved and improves the efficiency of the cluster. In this study, experiments on three kinds of machine learning algorithms show that this strategy can reduce the average response time of system in small-scale high-concurrency clusters.
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陈大才,吕立,高岑,孙咏.基于预测模型及独立训练节点的负载均衡策略.计算机系统应用,2018,27(9):220-223
CHEN Da-Cai,LYU Li,GAO Cen,SUN Yong.Load Balancing Strategy Based on Predictive Model and Independent Training Nodes.COMPUTER SYSTEMS APPLICATIONS,2018,27(9):220-223

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