基于压缩动量项的增量型ELM虚拟机能耗预测
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摘要
在基于基础设施即服务(Infrastructure as a service, IaaS)的云服务模式下,精准的虚拟机能耗预测,对于在众多物理服务器之间进行虚拟机调度策略的制定具有十分重要的意义.针对基于传统的增量型极限学习机(Incremental extreme learning machine, I-ELM)的预测模型存在许多降低虚拟机能耗预测准确性和效率的冗余节点,在现有I-ELM模型中加入压缩动量项将网络训练误差反馈到隐含层的输出中使预测结果更逼近输出样本,能够减少I-ELM的冗余隐含层节点,从而加快I-ELM的网络收敛速度,提高I-ELM的泛化性能.
In cloud service models which is based on infrastructure as a service(IaaS), how to accurately predict power of virtual machine is very important for making scheduling strategy of virtual machines among many physical servers.However, the traditional incremental extreme learning machine(I-ELM) includes too many redundant hidden nodes,resulting in decreased efficiency and accuracy of virtual machine power prediction. Connecting compression driving amount to I-ELM, the paper builds the intelligent prediction model of I-ELM based on the compression driving amount(CDAI-ELM), and uses the model for predicting virtual machine power.
In cloud service models which is based on infrastructure as a service(IaaS), how to accurately predict power of virtual machine is very important for making scheduling strategy of virtual machines among many physical servers.However, the traditional incremental extreme learning machine(I-ELM) includes too many redundant hidden nodes,resulting in decreased efficiency and accuracy of virtual machine power prediction. Connecting compression driving amount to I-ELM, the paper builds the intelligent prediction model of I-ELM based on the compression driving amount(CDAI-ELM), and uses the model for predicting virtual machine power.
引文
1 Wu Zhi-Xue. Advances on virtualization technology of cloud computing. Journal of Computer Applications, 2017, 37(4):915-923(武志学.云计算虚拟化技术的发展与趋势.计算机应用, 2017,37(4):915-923)
2 Cui Yong, Song Jian, Miao Cong-Cong, Tang Jun. Mobile cloud computing research progress and trends. Chinese Journal of Computers, 2017, 40(2):273-295(崔勇,宋健,缪葱葱,唐俊.移动云计算研究进展与趋势.计算机学报, 2017, 37(4):915-923)
3 Xia Yuan-Qing, Yan Ce, Wang Xiao-Jing, Song Xiang-Hui.Intelligent transportation cyber-physical cloud control systems. Acta Automatica Sinica, 2019, 45(1):132-142(夏元清,闫策,王笑京,宋向辉.智能交通信息物理融合云控制系统.自动化学报, 2019, 45(1):132-142)
4 Jin Shun-Fu, Hao Shan-Shan, Wang Bao-Shuai. Virtual machine scheduling strategy based on dual-speed and work vacation mode and its parameter optimization. Journal on Communications, 2017, 38(12):10-20(金顺福,郝闪闪,王宝帅.融合双速率和工作休眠的虚拟机调度策略及参数优化.通信学报, 2017, 38(12):10-20)
5 Zhao Chun, Yan Lian-Shan, Cui Yun-He, Xing Huan-Lai,Feng Bin. Dynamic adjusting threshold algorithm for virtual machine migration. Journal of Computer Applications,2017, 37(9):2547-2550(赵春,闫连山,崔允贺,邢焕来,冯斌.基于动态调整阈值的虚拟机迁移算法.计算机应用, 2017, 37(9):2547-2550)
6 Zhou Ping, Zhang Li, Li Wen-Peng, Dai Peng, Chai TianYou. Autoencoder and PCA based RVFLNs modeling for multivariate molten iron quality in blast furnace ironmaking. Acta Automatica Sinica, 2018, 44(10):1799-1811(周平,张丽,李温鹏,戴鹏,柴天佑.集成自编码与PCA的高炉多元铁水质量随机权神经网络建模.自动化学报, 2018, 44(10):1799-1811)
7 Tang Yi-Xuan, Xue Xiao-Ru, Yao Zhen, Xu Min, Zhang Yong-Mei, Zhang He-Liang, Xu Dao-Lei. An energy-aware VM deployment strategy for the resource pool of power information system. Electric Power Information and Communication Technology, 2017, 15(6):95-100(唐轶轩,薛晓茹,姚振,徐敏,张永梅,张禾良,徐道磊.电力信息系统资源池的能耗感知虚拟机部署策略.电力信息与通信技术, 2017,15(6):95-100)
8 Xu H. Research on neural network based on virtual machine power prediction model[Master thesis], Beijing University of Posts and Telecommunications, 2015.
9 赵雅倩.一种基于模糊神经网络的虚拟机能耗预测方法及系统,中国专利CN105975385A, 2016.09.28.
10 Jia Jiong-Hao, Chen Ning-Jiang, Li Xiang, Huang Ru-Wei.A dynamic adjustment strategy for virtual machines in cloud based on availability capability. Journal of Guangxi University(Nat Sci Ed), 2016, 41(3):796-803(贾炅昊,陈宁江,李湘,黄汝维.基于可用能力建模的云虚拟机动态调整策略.广西大学学报(自然科学版), 41(3):796-803)
11 Huang G B, Chen L, Siew C K. Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Transactions on Neural Networks, 2006, 17(4):879-892
12 Tian Z D, Li S J, Wang Y H, Sha Y. Short-term wind power prediction based on empirical mode decomposition and improved extreme learning machine. Journal of Electrical Engineering and Technology, 2018, 13(5):1841-1851
13 Tian Z D, Li S J, Wang Y H, Sha Y. An adaptive online sequential extreme learning machine for short-term wind speed prediction based on improved artificial bee colony algorithm. Neural Network World, 2018, 28(3):191-212
14 Tian Z D, Li S J, Wang Y H, Sha Y. Network traffic prediction method based on improved ABC algorithm optimized EM-ELM. Journal of China Universities of Posts and Telecommunications, 2018, 25(3):33-44
15 Tang X L, Han M. Partial Lanczos extreme learning machine for single output regression problems. Neurocomputing, 2009, 13(72):3066-3076
16 Han Min, Li De-Cai. An norm 1 regularization term ELM algorithm based on surrogate function and Bayesian framework. Acta Automatica Sinica, 2011, 37(11):1344-1350(韩敏,李德才.基于替代函数及贝叶斯框架的1范数ELM算法.自动化学报, 2011, 37(11):1344-1350)
17 Huang G B, Zhou H, Ding X, Zhang R. Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems, Man and Cybernetics, Part B(Cybernetics), 2012, 2(42):513-529
18 Chen C L P, Liu Z L. Broad learning system:An effective and efficient incremental learning system without the need for deep architecture. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(1):10-24
19 Sara S, Nicholas F, Geoffrey E H. Dynamic routing between capsules. In:Proceedings of the 31st Conference on Neural Information Processing Systems. California, USA, 2017.arXiv:1710.09829
20 Wang B. The approximation order of convex incremental extreme learning machine[Master thesis], Northwest University, 2015
21 Knowledge discovery in databases, http://www.kdd.org.htm
22 Tian Zhong-Da, Li Shu-Jiang, Wang Yan-Hong, Wang Xiang-Dong. Network traffic prediction based on ARIMA with Gaussian process regression compensation. Journal of Beijing University of Posts and Telecommunications, 2017,40(6):65-73(田中大,李树江,王艳红,王向东.高斯过程回归补偿ARIMA的网络流量预测.北京邮电大学学报, 2017, 40(6):65-73)
23 Tian Z D, Li S J, Wang Y H, Sha Y. A prediction method based on wavelet transform and multiple models fusion for chaotic time series. Chaos, Solitons and Fractals, 2017, 98:158-172
24 Tian Z D, Li S J, Wang Y H, Sha Y. Short-term wind speed prediction based on improved PSO algorithm optimized EM-ELM. Energy Sources, Part A:Recovery, Utilization, and Environmental Effects, 2019, 41(1):26-46
2 Cui Yong, Song Jian, Miao Cong-Cong, Tang Jun. Mobile cloud computing research progress and trends. Chinese Journal of Computers, 2017, 40(2):273-295(崔勇,宋健,缪葱葱,唐俊.移动云计算研究进展与趋势.计算机学报, 2017, 37(4):915-923)
3 Xia Yuan-Qing, Yan Ce, Wang Xiao-Jing, Song Xiang-Hui.Intelligent transportation cyber-physical cloud control systems. Acta Automatica Sinica, 2019, 45(1):132-142(夏元清,闫策,王笑京,宋向辉.智能交通信息物理融合云控制系统.自动化学报, 2019, 45(1):132-142)
4 Jin Shun-Fu, Hao Shan-Shan, Wang Bao-Shuai. Virtual machine scheduling strategy based on dual-speed and work vacation mode and its parameter optimization. Journal on Communications, 2017, 38(12):10-20(金顺福,郝闪闪,王宝帅.融合双速率和工作休眠的虚拟机调度策略及参数优化.通信学报, 2017, 38(12):10-20)
5 Zhao Chun, Yan Lian-Shan, Cui Yun-He, Xing Huan-Lai,Feng Bin. Dynamic adjusting threshold algorithm for virtual machine migration. Journal of Computer Applications,2017, 37(9):2547-2550(赵春,闫连山,崔允贺,邢焕来,冯斌.基于动态调整阈值的虚拟机迁移算法.计算机应用, 2017, 37(9):2547-2550)
6 Zhou Ping, Zhang Li, Li Wen-Peng, Dai Peng, Chai TianYou. Autoencoder and PCA based RVFLNs modeling for multivariate molten iron quality in blast furnace ironmaking. Acta Automatica Sinica, 2018, 44(10):1799-1811(周平,张丽,李温鹏,戴鹏,柴天佑.集成自编码与PCA的高炉多元铁水质量随机权神经网络建模.自动化学报, 2018, 44(10):1799-1811)
7 Tang Yi-Xuan, Xue Xiao-Ru, Yao Zhen, Xu Min, Zhang Yong-Mei, Zhang He-Liang, Xu Dao-Lei. An energy-aware VM deployment strategy for the resource pool of power information system. Electric Power Information and Communication Technology, 2017, 15(6):95-100(唐轶轩,薛晓茹,姚振,徐敏,张永梅,张禾良,徐道磊.电力信息系统资源池的能耗感知虚拟机部署策略.电力信息与通信技术, 2017,15(6):95-100)
8 Xu H. Research on neural network based on virtual machine power prediction model[Master thesis], Beijing University of Posts and Telecommunications, 2015.
9 赵雅倩.一种基于模糊神经网络的虚拟机能耗预测方法及系统,中国专利CN105975385A, 2016.09.28.
10 Jia Jiong-Hao, Chen Ning-Jiang, Li Xiang, Huang Ru-Wei.A dynamic adjustment strategy for virtual machines in cloud based on availability capability. Journal of Guangxi University(Nat Sci Ed), 2016, 41(3):796-803(贾炅昊,陈宁江,李湘,黄汝维.基于可用能力建模的云虚拟机动态调整策略.广西大学学报(自然科学版), 41(3):796-803)
11 Huang G B, Chen L, Siew C K. Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Transactions on Neural Networks, 2006, 17(4):879-892
12 Tian Z D, Li S J, Wang Y H, Sha Y. Short-term wind power prediction based on empirical mode decomposition and improved extreme learning machine. Journal of Electrical Engineering and Technology, 2018, 13(5):1841-1851
13 Tian Z D, Li S J, Wang Y H, Sha Y. An adaptive online sequential extreme learning machine for short-term wind speed prediction based on improved artificial bee colony algorithm. Neural Network World, 2018, 28(3):191-212
14 Tian Z D, Li S J, Wang Y H, Sha Y. Network traffic prediction method based on improved ABC algorithm optimized EM-ELM. Journal of China Universities of Posts and Telecommunications, 2018, 25(3):33-44
15 Tang X L, Han M. Partial Lanczos extreme learning machine for single output regression problems. Neurocomputing, 2009, 13(72):3066-3076
16 Han Min, Li De-Cai. An norm 1 regularization term ELM algorithm based on surrogate function and Bayesian framework. Acta Automatica Sinica, 2011, 37(11):1344-1350(韩敏,李德才.基于替代函数及贝叶斯框架的1范数ELM算法.自动化学报, 2011, 37(11):1344-1350)
17 Huang G B, Zhou H, Ding X, Zhang R. Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems, Man and Cybernetics, Part B(Cybernetics), 2012, 2(42):513-529
18 Chen C L P, Liu Z L. Broad learning system:An effective and efficient incremental learning system without the need for deep architecture. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(1):10-24
19 Sara S, Nicholas F, Geoffrey E H. Dynamic routing between capsules. In:Proceedings of the 31st Conference on Neural Information Processing Systems. California, USA, 2017.arXiv:1710.09829
20 Wang B. The approximation order of convex incremental extreme learning machine[Master thesis], Northwest University, 2015
21 Knowledge discovery in databases, http://www.kdd.org.htm
22 Tian Zhong-Da, Li Shu-Jiang, Wang Yan-Hong, Wang Xiang-Dong. Network traffic prediction based on ARIMA with Gaussian process regression compensation. Journal of Beijing University of Posts and Telecommunications, 2017,40(6):65-73(田中大,李树江,王艳红,王向东.高斯过程回归补偿ARIMA的网络流量预测.北京邮电大学学报, 2017, 40(6):65-73)
23 Tian Z D, Li S J, Wang Y H, Sha Y. A prediction method based on wavelet transform and multiple models fusion for chaotic time series. Chaos, Solitons and Fractals, 2017, 98:158-172
24 Tian Z D, Li S J, Wang Y H, Sha Y. Short-term wind speed prediction based on improved PSO algorithm optimized EM-ELM. Energy Sources, Part A:Recovery, Utilization, and Environmental Effects, 2019, 41(1):26-46
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