[1]赵 莉.基于支持向量机的云计算资源负载预测模型[J].南京理工大学学报(自然科学版),2018,42(06):687.[doi:10.14177/j.cnki.32-1397n.2018.42.06.008]
 Zhao Li.Load forecasting model of cloud computing resourcesbased on support vector machine[J].Journal of Nanjing University of Science and Technology,2018,42(06):687.[doi:10.14177/j.cnki.32-1397n.2018.42.06.008]
点击复制

基于支持向量机的云计算资源负载预测模型()
分享到:

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
42卷
期数:
2018年06期
页码:
687
栏目:
出版日期:
2018-12-30

文章信息/Info

Title:
Load forecasting model of cloud computing resourcesbased on support vector machine
文章编号:
1005-9830(2018)06-0687-06
作者:
赵 莉
信阳农林学院 信息工程学院,河南 信阳 464000
Author(s):
Zhao Li
College of Information Engineering,Xinyang Agriculture and Forestry University,Xinyang 464000,China
关键词:
支持向量机 云计算 资源 负载预测 混沌分析算法 组合核函数
Keywords:
support vector machine cloud computing resources load prediction chaotic analysis algorithm combined kernel function
分类号:
TP391
DOI:
10.14177/j.cnki.32-1397n.2018.42.06.008
摘要:
为了准确描述云计算资源负载的动态变化趋势,设计了云计算资源负载预测模型。采用混沌分析算法对云计算资源负载的时间序列进行处理,构建云计算资源负载预测的学习样本。采用支持向量机(SVM)建立云计算资源负载的预测模型,并设计了组合核函数,以提高SVM的学习能力。选择灰色模型、 反向传播(BP)神经网络、径向基函数(RBF)神经网络、RBF核函数的支持向量机进行云计算资源负载预测的仿真对比实验。结果表明,对单步云计算资源负载预测时,该文模型的预测精度为94.85%,仅低于灰色模型的95.85%; 对多步云计算资源负载预测时,该文模型的预测精度最高,为89.17%。
Abstract:
A cloud computing resource load forecasting model is designed to describe the dynamic change trend of a cloud computing resource load accurately. The load time sequence of the cloud computing resource is handled by the chaotic analysis algorithm,and learning samples of cloud computing resource load forecasting is constructed. A cloud computing source load forecasting model is established by the support vector machine(SVM),and a combination kernel function is designed to improve the learning ability of the SVM. The simulation comparison experiments for cloud computing resource load forecasting are carried out comparing with the SVM of the gray model,the back propagation(BP)neural network,the radial basis function(RBF)neural network and the RBF kernel function. The results show that the accuracy of this model is 94.85% for single step cloud computing resource load forecasting,and is lower than that of the gray model(95.85%)only; the accuracy of this model is 89.17% for multi-step cloud computing resource load forecasting,and is the highest.

参考文献/References:

[1] Buyya R,Yeo C S,Venugopal S,et al. Cloud computing and emerging IT platforms:Vision,hype,and reality for delivering computing as the 5th utility[J]. Future Generation Computer Systems,2009,25(6):599-616.
[2]Tang Shanjiang,Lee B,He Bingsheng. Dynamic MR:A dynamic slot allocation optimization framework for map reduce clusters[J]. IEEE Transactions on Cloud Computing,2014,2(3):333-347.
[3]Ahn T H,Sandu A,Watson L T,et al. A framework to analyze the performance of load balancing schemes for ensembles of stochastic simulations[J]. International Journal of Parallel Programming,2015,43(4):597-630.
[4]刘文娟,陈华平,郝尚刚.云平台下满足任务截止时间的资源分配策略[J]. 计算机工程,2012,38(6):60-63.
Liu Wenjuan,Chen Huaping,Hao Shanggang. Resource allocation strategy for meeting task deadline on cloud platform[J]. Computer Engineering,2012,38(6):60-63.
[5]黄纬,温志萍,程初.云计算中基于K-均值聚类的虚拟机调度算法研究[J]. 南京理工大学学报,2013,37(6):55-56.
Huang Wei,Wen Zhiping,Cheng Chu. Virtual machine scheduling algorithm based on K-means clustering in cloud computing[J]. Journal of Nanjing University of Science and Technology,2013,37(6):55-56.
[6]王倩,石振国,孙万捷,等. 基于PEPA的云计算资源分配算法性能评价[J]. 计算机应用研究,2015,32(4):1179-1183.
Wang Qian,Shi Zhenguo,Sun Wanjie,et al. PEPA model approach for performance evaluation of dynamic resource provision in cloud computing[J]. Application Research of Computers,2015,32(4):1179-1183.
[7]徐达宇,丁帅.改进GWO优化SVM的云计算资源负载短期预测研究[J]. 计算机工程与应用,2017,53(7):68-73.
Xu Dayu,Ding Shuai. Research on improved GWO-optimized SVM-based short-term load prediction for cloud computing[J]. Computer Engineering and Applications,2017,53(7):68-73.
[8]魏亮,黄韬,陈建亚,等. 基于工作负载预测的虚拟机整合算法[J]. 电子与信息学报,2013,35(6):1271-1276.
Wei Liang,Huang Tao,Chen Jianya,et al. Work load prediction-based algorithm for consolidation of virtual machines[J]. Journal of Electronics & Information Technology,2013,35(6):1271-1276.
[9]王浩,罗宇.基于负载预测的虚拟机动态调度算法研究与实现[J]. 计算机工程与科学,2016,38(10):1974-1979.
Wang Hao,Luo Yu. A virtual machine dynamic scheduling algorithm based on load forecast[J]. Computer Engineering & Science,2016,38(10):1974-1979.
[10]刘亚秋,邢乐乐,景维鹏.云计算环境下基于时间期限和预算的调度算法[J]. 计算机工程,2013,39(6):56-59.
Liu Yaqiu,Xing Lele,Jing Weipeng. Schedule algorithm based on deadline and budget under cloud computing environment[J]. Computer Engineering,2013,39(6):56-59.
[11]余剑武,胡其丰,文丞,等. 基于支持向量机的电火花加工8418钢表面粗糙度预测模型[J]. 中国机械工程,2018,29(7):771-774.
Yu Jianwu,Hu Qifeng,Wen Cheng,et al. Prediction model of surface roughness of 8418 steel by EDM based on SVM[J]. China Mechanical Engineering,2018,29(7):771-774.
[12]樊泽凯,贾红丽.基于布谷鸟搜索算法和支持向量机的故障预测模型研究[J]. 军事运筹与系统工程,2017,31(2):66-70.
Fan Zekai,Jia Hongli. Research on fault prediction model based on the cuckoo search algorithm and support vector machine[J]. Military Operations Research and Systems Engineering,2017,31(2):66-70.
[13]申京,吴晨光,郝洋,等. 面向云计算数据中心的弹性资源调整方法[J]. 南京理工大学学报,2015,39(1):122-126.
Shen Jing,Wu Chenguang,Hao Yang,et al. Elastic resource adjustment method for cloud computing data center[J]. Journal of Nanjing University of Science and Technology,2015,39(1):122-126.

相似文献/References:

[1]周晓剑,马义中,刘利平,等.基于梯度信息的最小二乘支持向量回归机[J].南京理工大学学报(自然科学版),2011,(01):138.
 ZHOU Xiao-jian,MA Yi-zhong,LIU Li-ping,et al.Gradient-enhanced Least Squares Support Vector Regression[J].Journal of Nanjing University of Science and Technology,2011,(06):138.
[2]陈沅涛,徐蔚鸿,吴佳英.一种增量向量支持向量机学习算法[J].南京理工大学学报(自然科学版),2012,36(05):873.
 CHEN Yuan-tao,XU Wei-hong,WU Jia-ying.Incremental Vector Support Vector Machine Learning Algorithm[J].Journal of Nanjing University of Science and Technology,2012,36(06):873.
[3]陈沅涛,徐蔚鸿,吴佳英,等.基于增量学习向量SVM方法的图像分割应用[J].南京理工大学学报(自然科学版),2014,38(01):6.
 Chen Yuantao,Xu Weihong,Wu Jiaying,et al.Image segmentation application based on incremental learning vector SVM algorithm[J].Journal of Nanjing University of Science and Technology,2014,38(06):6.
[4]杨赛,赵春霞.基于空间概率乘积核函数的图像分类算法[J].南京理工大学学报(自然科学版),2014,38(03):325.
 Yang Sai,Zhao Chunxia.Image classification algorithm based on spatial probability product kernel[J].Journal of Nanjing University of Science and Technology,2014,38(06):325.
[5]孙炯宁.基于混合式子树算法的大数据匿名化[J].南京理工大学学报(自然科学版),2015,39(05):609.
 Sun Jiongning.Anonymization of big data based on hybrid tree[J].Journal of Nanjing University of Science and Technology,2015,39(06):609.
[6]王 倩,谭永杰,秦 杰,等.基于Hadoop分布式平台的海量图像检索[J].南京理工大学学报(自然科学版),2017,41(04):442.[doi:10.14177/j.cnki.32-1397n.2017.41.04.007]
 Wang Qian,Tan Yongjie,Qin Jie,et al.Massive image retrieval based on Hadoop distributed platform[J].Journal of Nanjing University of Science and Technology,2017,41(06):442.[doi:10.14177/j.cnki.32-1397n.2017.41.04.007]
[7]黄 纬,张建德,彭焕峰,等.数据中心应用感知的动态资源配置研究[J].南京理工大学学报(自然科学版),2018,42(03):322.[doi:10.14177/j.cnki.32-1397n.2018.42.03.010]
 Huang Wei,Zhang Jiande,Peng Huanfeng,et al.Application-aware dynamic resource allocation in data center[J].Journal of Nanjing University of Science and Technology,2018,42(06):322.[doi:10.14177/j.cnki.32-1397n.2018.42.03.010]

备注/Memo

备注/Memo:
收稿日期:2018-05-12 修回日期:2018-07-25
基金项目:河南省重点研发与推广专项项目(182102210131); 河南省高等教育教学改革研究与实践项目(2017SJGLX389); 河南省政府决策研究招标课题(2018B145)
作者简介:赵莉(1973-),女,副教授,主要研究方向:云计算、Moodle、计算机网络,E-mail:menger030302@163.com。
引文格式:赵莉. 基于支持向量机的云计算资源负载预测模型[J]. 南京理工大学学报,2018,42(6):687-692.
投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2018-12-30