| 基于预测模型及独立训练节点的负载均衡策略 |
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| 引用本文： | 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[J].计算机系统应用,2018,27(9):220-223. |
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| 作者姓名： | 陈大才 吕立 高岑 孙咏 |
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| 作者单位： | 中国科学院大学, 北京 100049;中国科学院 沈阳计算技术研究所, 沈阳 110168,中国科学院 沈阳计算技术研究所, 沈阳 110168,中国科学院 沈阳计算技术研究所, 沈阳 110168,中国科学院 沈阳计算技术研究所, 沈阳 110168 |
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| 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.
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| 关 键 词： | 负载均衡 机器学习 最少响应时间 |
| 收稿时间： | 2018/1/7 0:00:00 |
| 修稿时间： | 2018/2/9 0:00:00 |
Load Balancing Strategy Based on Predictive Model and Independent Training Nodes |
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| CHEN Da-Cai,LYU Li,GAO Cen and SUN Yong.Load Balancing Strategy Based on Predictive Model and Independent Training Nodes[J].Computer Systems& Applications,2018,27(9):220-223. |
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| Authors: | CHEN Da-Cai LYU Li GAO Cen and SUN Yong |
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| Affiliation: | University of Chinese Academy of Sciences, Beijing 100049, China;Shenyang Institute of Computer Technology, Chinese Academy of Sciences, Shenyang 110168, China,Shenyang Institute of Computer Technology, Chinese Academy of Sciences, Shenyang 110168, China,Shenyang Institute of Computer Technology, Chinese Academy of Sciences, Shenyang 110168, China and Shenyang Institute of Computer Technology, Chinese Academy of Sciences, Shenyang 110168, China |
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| 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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| Keywords: | load balancing machine learning least response time |
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