基于朴素贝叶斯算法的iVCE资源评价模型

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• 英文论文题名：IVCE resource evaluation model based on naive bayesian algorithm
• 论文作者：朱晓宁 ; 孙斌 ; 朱春鸽
• 年：2016
• 作者机构：北京邮电大学信息安全中心;北京软安科技有限公司;
• 论文关键词：贝叶斯 ; iVCE ; 云计算 ; 移动云计算 ; 资源评价
• 英文论文关键词：Naive Bayes classifier NBC ; iVCE ; cloud computing ; resource evaluation
• 会议召开时间：2016-06-24
• 会议录名称：2016年全国通信软件学术会议程序册与交流文集
• 语种：中文
• 分类号：TP301.6
• 学会代码：ZGTH
• 会议名称：2016年全国通信软件学术会议
• 会议地点：中国陕西西安
• 主办单位：中国通信学会
• 学会名称：中国通信学会
• 页数：6
• 文件大小：432k
• 原文格式：O
• 会议级别：全国

摘要

互联网的计算资源具有数量大、类型多、随机性强、稳定性相对较差等特点,本文提出一种基于朴素贝叶斯分类的iVCE~([1][2])云平台资源可靠性评价算法。算法通过对计算资源的特征提取,离散化处理后,使用概率估计方法对资源的状态做出实时的评价,结过在iVCE平台的实际运行效果表明,算法使平台任务执行的成功率和工作效率分别提高了30%和8%左右,满足了实际生产的需求。
The computing resources of the Internet has a large number,type,strong randomness,more stability are relatively poor,this paper puts forward a kind of based on naive bayesian classification iVCE~([1][2]) cloud platform reliability evaluation algorithm.Feature extraction algorithm based on computing resources,normalized processing,a form of state real-time assessment of resources,through practical operation effect shows that the iVCE platform algorithm effectively improve the platform's task execution success rate and efficiency,to meet the needs of actual production.

引文

[1]Xicheng Lu,Huaimin Wang,Ji Wang,等.Internet-based virtual computing environment(iVCE):Concepts and architecture[J].Science in China,2006,49(6):681-701.
    [2]IEEE.iVCE 2015 Committees[C]//2015 IEEE Symposium on Service-Oriented System Engineering(SOSE).IEEE Computer Society,2015:xvi-xvi.
    [3]Prudencio E E,Bauman P T,Faghihi D,et al.A computational framework for dynamic data-driven material damage control,based on Bayesian inference and model selection+[J].International Journal for Numerical Methods in Engineering,2015,102(3-4):379-403.
    [4]Araki T,Ikeda K,Akaho S.An efficient sampling algorithm with adaptations for Bayesian variable selection[J].Neural Networks,2015,61:22-31.
    [5]Wong T T.Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation[J].Pattern Recognition,2015,48(9):2839-2846.
    [6]Pang S,Ban T,Kadobayashi Y,et al.Personalized mode transductive spanning SVM classification tree[J].Information Sciences,2015,181(11):2071-2085.
    [7]K.Dehghanpour,M.H.Nehrir,J.W.Sheppard,et al.Agent-Based Modeling in Electrical Energy Markets Using Dynamic Bayesian Networks[J].IEEE Transactions on Power Systems,2016:1-11.