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Load forecasting model of cloud computing resourcesbased on support vector machine(PDF)


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Load forecasting model of cloud computing resourcesbased on support vector machine
Zhao Li
College of Information Engineering,Xinyang Agriculture and Forestry University,Xinyang 464000,China
support vector machine cloud computing resources load prediction chaotic analysis algorithm combined kernel function
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.


[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.


Last Update: 2018-12-30