Performance Prediction of Network-Intensive Systems in Cloud Environment: A Bayesian Approach

Ying Fan, Jianpeng Hu, Tianqi Sun
{"title":"Performance Prediction of Network-Intensive Systems in Cloud Environment: A Bayesian Approach","authors":"Ying Fan, Jianpeng Hu, Tianqi Sun","doi":"10.1109/EEI48997.2019.00085","DOIUrl":null,"url":null,"abstract":"The resource requirements of cloud applications varies with the load intensity, so it is necessary to predict resource changes in advance. In this paper, a Bayesian method is proposed to predict the bandwidth consumption and quality of service. First, log mining is used to extract feature variables, and a Bayesian model is constructed by using stepwise discriminant method to select characteristic variables that are crucial for prediction. Then Bayesian discriminant and Bayesian network are used to predict the bandwidth consumption and response time. At last, we use a real-running web system to evaluate the effectiveness and accuracy of this method. Comparing with other methods, it can get more accurate prediction result of the performance in the cloud environment.","PeriodicalId":150974,"journal":{"name":"2019 International Conference on Electronic Engineering and Informatics (EEI)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 International Conference on Electronic Engineering and Informatics (EEI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EEI48997.2019.00085","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

The resource requirements of cloud applications varies with the load intensity, so it is necessary to predict resource changes in advance. In this paper, a Bayesian method is proposed to predict the bandwidth consumption and quality of service. First, log mining is used to extract feature variables, and a Bayesian model is constructed by using stepwise discriminant method to select characteristic variables that are crucial for prediction. Then Bayesian discriminant and Bayesian network are used to predict the bandwidth consumption and response time. At last, we use a real-running web system to evaluate the effectiveness and accuracy of this method. Comparing with other methods, it can get more accurate prediction result of the performance in the cloud environment.
云环境下网络密集型系统的性能预测:贝叶斯方法
云应用的资源需求随着负载强度的变化而变化,因此需要提前预测资源的变化。本文提出了一种贝叶斯方法来预测带宽消耗和服务质量。首先,利用日志挖掘方法提取特征变量,利用逐步判别法构建贝叶斯模型,选择对预测有重要影响的特征变量;然后利用贝叶斯判别法和贝叶斯网络对带宽消耗和响应时间进行预测。最后,通过一个实际运行的web系统,对该方法的有效性和准确性进行了验证。与其他方法相比,该方法可以得到更准确的云环境下的性能预测结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信