首页|期刊简介|投稿指南|分类索引|刊文选读|订阅指南|证明资料|样刊邮寄查询|常见问题解答|联系我们
申扬,于海,尹晓华. 基于机器学习的电网信息通信服务器智能优化[J]. 科学技术与工程, 2020, 20(32): 13302-13308.
shenyang,于海,尹晓华.Research on intelligent optimization of information communication server of power grid based on machine learning[J].Science Technology and Engineering,2020,20(32):13302-13308.
基于机器学习的电网信息通信服务器智能优化
Research on intelligent optimization of information communication server of power grid based on machine learning
投稿时间:2020-01-09  修订日期:2020-07-28
DOI:
中文关键词:  服务器智能优化  支持向量机  流体搜索优化算法  信息通信服务器  线程池调优
英文关键词:intelligent optimization of server  support vector machine  fluid search optimization algorithm  information communication server  thread pool optimization
基金项目:国家自然科学基金(51437003)
        
作者单位
申扬 国网辽宁省电力有限公司信息通信分公司
于海 国网辽宁省电力有限公司信息通信分公司
尹晓华 国网辽宁省电力有限公司信息通信分公司
摘要点击次数: 187
全文下载次数: 81
中文摘要:
      随着我国电网向智能化、网络化、自动化发展,电网信息通信服务器承载着电网信息网络信息传输中的核心业务。信通服务器一般采用线程池技术来应对逐渐增多的用户请求,而选择合适的线程池尺寸成为了决定服务器性能的关键因素。本文提出一种基于支持向量机的信通服务器动态线程池智能优化模型,来动态减少用户的响应时间。首先,通过大量的信通服务器性能实验数据构造原始训练样本集,然后经过改进的流体优化算法搜索支持向量机的最优超参数,最后通过训练好的支持向量机预测不同电网用户场景下的最优线程池尺寸,从而实现对信通服务器的智能优化。通过辽宁省电网信通服务器的实验表明,基于改进流体优化算法的支持向量机智能线程池技术获得了更高的预测精度,减少了服务器的用户响应时间。
英文摘要:
      With China's power grid becoming more intelligent, networked and automatized, the information communication server (ICS) plays a key role in the information transmission of power grid. Generally, the thread pool technology is used in ICS to deal with the increasing users’ requests, and the key of server performance is how to choose the appropriate size of the thread pool. So an intelligent optimization model of dynamic thread pool in ICS based on support vector machine (SVM) was proposed to reduce the response time in this paper. First, the original training set was made from a large amount of experimental data of the performance of ICS, and then the fluid optimization algorithm (FSO) was improved to search the optimal hyper parameters of SVM. Finally, the trained SVM was used to predict the best size of thread pool in different user scenarios, so that the optimization of ICS was accomplished intelligently. The experiments on ICS of Liaoning Power Grid show that the intelligent thread pool technology based on SVM and IFSO achieves higher prediction accuracy and reduces the response time of the server.
查看全文  查看/发表评论  下载PDF阅读器
关闭
你是第35652122位访问者
版权所有:科学技术与工程编辑部
主管:中国科学技术协会    主办:中国技术经济学会
Tel:(010)62118920 E-mail:stae@vip.163.com
京ICP备05035734号-4
技术支持:本系统由北京勤云科技发展有限公司设计

京公网安备 11010802029091号