[1] |
Huang Chun-qiu,Chen Zhi,Rong Chui-tian.An elasticity and deadline-aware job scheduling algorithm[J].Computer and Modernization,2019(4):30-37.(in Chinese)
|
[2] |
He Fa-zhe. Design and implementation of YARN resource scheduling strategy optimization method[D].Harbin:Harbin Institute of Technology,2019.(in Chinese)
|
[3] |
Singh P,Gupta P,Jyoti K.TASM:Technocrat ARIMA and SVR model for workload prediction of web applications in cloud[J].Cluster Computing,2019,22(2):619-633.
|
[4] |
Wang Ying-wei,Ma Shu-cai.Time series forecasting based on ARIMA-LSTM hybrid model[J].Computer Applications and Software,2021,38(2):291-298.(in Chinese)
|
[5] |
Zhang W Q,Xu C.Time series forecasting method based on Huang transform and BP neural network[C]∥Proc of International Conference on Computational Intelligence and Secu- rity, 2011:497-502.
|
[6] |
Huang M X,Bao Q L,Zhang Y,et al.A hybrid algorithm for forecasting financial time series data based on DBSCAN and SVR[J].Information,2019,10(3):103-123.
|
[7] |
Hochreiter S,Schmidhuber J.Long short-term memory[J].Neural Computation,1997,9(8):1735-1780.
|
[8] |
Ye Yi,Huang Jian-bao,Tang Bin-bin, et al.Data mining of urban traffic based on Hadoop platform and LSTM model[J].Modern Computer,2020(34):22-26.(in Chinese)
|
[9] |
Liu H J,Xu H Z,Yan Y,et al.Bus arrival time prediction based on LSTM and spatial-temporal feature vector[J].IEEE Access,2020,8:11917-11929.
|
[10] |
Li Gao-sheng,Peng Ling,Li Xiang,et al.Study on short-term traffic forecast of urban bus stations based on LSTM[J].Journal of Highway and Transportation Research and Development,2019,36(2):128-135.(in Chinese)
|
[11] |
Zhao Jin-chao.Energy consumption data analysis and platform design based on hybrid model LSTM[D].Qingdao:Qingdao University of Science & Technology,2020.(in Chinese)
|
[12] |
Zhuang Jia-yi,Yang Guo-hua,Zheng Hao-feng,et al.Short-term load forecasting method based on multi-model fusion using CNN-LSTM-XGBoost framework[J].Electric Power,2021,54(5):46-55.(in Chinese)
|
[13] |
Qiu Rui-dong,He Shan,Dong Ning,et al.Irradiation intensity prediction of photovoltaic power station based on LSTM-LGB model[J].Journal of Anhui University (Natural Science Edition),2021,45(3):66-71.(in Chinese)
|
[14] |
Ma Lei,Huang Wei,Li Ke-cheng,et al.Photovoltaic ultra-short-term power prediction model based on Attention-LSTM[J].Electrical Measurement & Instrumentation,2021,58(2):146-152.(in Chinese)
|
[15] |
Guo Xu-dong,Song Liu-yang,Wang Hua-qing,et al.Remain- ing useful life prediction method based on improved CNN-LSTM[J].Measurement & Control Technology,2021,40(5):21-26.(in Chinese)
|
[16] |
Kang Xuan-ye,Zhao Lin-hai,Meng Jing-hui,et al.LSTM-based forecasting for number of faults of track circuit compensation capacitor[J].Journal of the China Railway Society,2021,43(1):94-99.(in Chinese)
|
[17] |
Shen Yan-bin,Zhang Xiao-li,Xia Yong,et al.Bi-LSTM neural network for remaining useful life prediction of bearings[J].Journal of Vibration Engineering,2021,34(2):411-420.(in Chinese)
|
[18] |
Dou Shan,Zhang Guang-yu,Xiong Zhi-hua.Anomaly detection of process unit based on LSTM time series reconstruction[J].CIESC Journal,2019,70(2):481-486.(in Chinese)
|
[19] |
Chen Xue-bin,Wu Jing,Xu Ming-dong.Individual default risk measurement and prevention of China’s credit bonds-based on LSTM deep learning model[J].Fudan Journal(Social Sciences Edition),2021,63(3):159-173.(in Chinese)
|
[20] |
Peng Yan,Liu Yu-hong,Zhang Rong-fen.Modeling and analysis of stock price forecast based on LSTM[J].Computer Engineering and Applications,2019,55(11):209-212.(in Chinese)
|
[21] |
Liao Zhi-wei,Chen Lin-tao,Huang Jie-dong,et al.Medium and short-term electricity coal price forecast based on feature space transformation and LSTM[J].Journal of Northeastern University(Natural Science),2021,42(4):483-493.(in Chinese)
|
[22] |
Han Qing-liang.Research on energy saving of Hadoop cluster based on neural network LSTM[D].Qingdao:Shandong University of Science and Technology,2018.(in Chinese)
|
[23] |
Gao J C,Wang H Y,Shen H Y.Task failure prediction in cloud data centers using deep learning[J].IEEE Transactions on Services Computing,2020,15(3):1411-1422.
|
[24] |
Ruan L,Bai Y,Li S N,et al.Workload time series prediction in storage systems:A deep learning based approach[J].Cluster Computing,2021.doi:10.100.1007/S10586-020-03214-y.
|
[25] |
Gupta S,Dileep A D,Gonsalves T A.Online sparse BLSTM models for resource usage prediction in cloud data centers[J].IEEE Transactions on Network and Service Management,2020,17(4):2335-2349.
|
[26] |
Thonglek K,Ichikawa K,Takahashi K,et al.Improving resource utilization in data centers using an LSTM-based prediction model[C]∥Proc of 2019 IEEE International Confe- rence on Cluster Computing,2019:1-8.
|
[27] |
Greff K,Srivastava R K,Koutník J,et al.LSTM:A search space odyssey[J].IEEE Transactions on Neural Networks and Learning Systems,2016,28(10):2222-2232.
|
[28] |
Hu Ya-hong,Sheng Xia,Mao Jia-fa.Task scheduling optimization in Spark environment with unbalanced resources[J].Computer Engineering & Science,2020,42(2):203-209.(in Chinese)
|
[29] |
Zhang Y Y,Sun W,Inoguchi Y.Predict task running time in grid environments based on CPU load predictions[J].Future Generation Computer Systems,2008,24(6):489-497.
|
[30] |
Chen K,Zhou Y,Dai F Y.A LSTM-based method for stock returns prediction:A case study of China stock market[C]∥Proc of 2015 IEEE International Conference on Big Data,2015:2823-2824.
|
[31] |
Xia Ke-wen,Li Chang-biao,Shen Jun-yi.An optimization algorithm on the number of hidden layer nodes in feed- forward neural network[J].Computer Science,2005,32(10):143-145.(in Chinese)
|
|
附中文参考文献:
|
[1] |
黄春秋,陈志,荣垂田.一种作业弹性与截止时间感知的作业调度算法[J].计算机与现代化,2019(4):30-37.
|
[2] |
何发哲.YARN资源调度策略优化方法的设计与实现[D].哈尔滨:哈尔滨工业大学,2019.
|
[4] |
王英伟,马树才.基于ARIMA 和LSTM 混合模型的时间序列预测[J].计算机应用与软件,2021,38(2):291-298.
|
[8] |
叶奕,黄检宝,唐斌斌,等.基于Hadoop平台及LSTM模型的城市交通出行数据挖掘[J].现代计算机,2020(34):22-26.
|
[10] |
李高盛,彭玲,李祥,等.基于LSTM 的城市公交车站短时客流量预测研究[J].公路交通科技,2019,36(2):128-135.
|
[11] |
赵金超.基于LSTM混合模型的能耗数据分析与平台设计[D].青岛:青岛科技大学,2020.
|
[12] |
庄家懿,杨国华,郑豪丰,等.基于多模型耦合的CNN-LSTM-XGBoost短期电力负荷预测方法[J].中国电力,2021,54(5):46-55.
|
[13] |
邱瑞东,何山,董宁,等.基于LSTM-LGB 模型的光伏电站辐照强度预测[J].安徽大学学报(自然科学版),2021,45(3):66-71.
|
[14] |
马磊,黄伟,李克成,等.基于Attention-LSTM的光伏超短期功率预测模型[J].电测与仪表,2021,58(2):146-152.
|
[15] |
郭旭东,宋浏阳,王华庆,等.基于改进CNN-LSTM的剩余使用寿命预测方法[J].测控技术,2021,40(5):21-26.
|
[16] |
康玄烨,赵林海,孟景辉,等.基于LSTM的轨道电路补偿电容故障数量预测[J].铁路学报,2021,43(1):94-99.
|
[17] |
申彦斌,张小丽,夏勇,等.Bi-LSTM神经网络用于轴承剩余使用寿命预测研究[J].振动工程学报,2021,34(2):411-420.
|
[18] |
窦珊,张广宇,熊智华.基于LSTM时间序列重建的生产装置异常检测[J].化工学报,2019,70(2):481-486.
|
[19] |
陈学彬,武靖,徐明东.我国信用债个体违约风险测度与防范—基于LSTM深度学习模型[J].复旦学报(社会科学版),2021,63(3):159-173.
|
[20] |
彭燕,刘宇红,张荣芬.基于LSTM的股票价格预测建模与分析[J].计算机工程与应用,2019,55(11):209-212.
|
[21] |
廖志伟,陈琳韬,黄杰栋,等.基于特征空间变换与LSTM的中短期电煤价格预测[J].东北大学学报(自然科学版),2021,42(4):483-493.
|
[22] |
韩庆亮.基于LSTM神经网络的Hadoop集群节能问题研究[D].青岛:山东科技大学,2018.
|
[28] |
胡亚红,盛夏,毛家发.资源不均衡Spark 环境任务调度优化算法研究[J].计算机工程与科学,2020,42(2):203-209.
|
[31] |
夏克文,李昌彪,沈钧毅.前向神经网络隐含层节点数的一种优化算法[J].计算机科学,2005,32(10):143-145.
|