Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (5): 1508-1515.DOI: 10.11772/j.issn.1001-9081.2021030393
• Advanced computing • Previous Articles Next Articles
Yifei WANG1, Lei YU2,3, Fei TENG1(), Jiayu SONG1, Yue YUAN1
Received:
2021-03-16
Revised:
2021-06-08
Accepted:
2021-06-11
Online:
2022-06-11
Published:
2022-05-10
Contact:
Fei TENG
About author:
WANG Yifei, born in 1996, M. S. candidate. Her researchinterests include cloud computing,big data mining.Supported by:
通讯作者:
滕飞
作者简介:
王艺霏(1996—),女,山西吕梁人,硕士研究生,主要研究方向:云计算、大数据挖掘基金资助:
CLC Number:
Yifei WANG, Lei YU, Fei TENG, Jiayu SONG, Yue YUAN. Resource load prediction model based on long-short time series feature fusion[J]. Journal of Computer Applications, 2022, 42(5): 1508-1515.
王艺霏, 于雷, 滕飞, 宋佳玉, 袁玥. 基于长-短时序特征融合的资源负载预测模型[J]. 《计算机应用》唯一官方网站, 2022, 42(5): 1508-1515.
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URL: http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2021030393
网络层 | 参数 |
---|---|
ConvLSTM2D | 卷积核大小为64,卷积步长为(3,3) |
LSTM(两层) | 隐藏层为10 |
FC(两层) | 输入维度为36,输出维度为6 |
Tab. 1 ConvLSTM channel configuration
网络层 | 参数 |
---|---|
ConvLSTM2D | 卷积核大小为64,卷积步长为(3,3) |
LSTM(两层) | 隐藏层为10 |
FC(两层) | 输入维度为36,输出维度为6 |
网络层 | 参数 |
---|---|
LSTM1 | 隐藏层为36 |
LSTM2 | 隐藏层为20 |
FC | 输出维度为6 |
Tab. 2 LSTM channel configuration
网络层 | 参数 |
---|---|
LSTM1 | 隐藏层为36 |
LSTM2 | 隐藏层为20 |
FC | 输出维度为6 |
模型 | RMSE | MAE | R2 |
---|---|---|---|
LSTM(单通道)[ | 6.167 | 4.492 | 0.658 0 |
CNN(单通道)[ | 6.975 | 5.131 | 0.562 7 |
CNN+LSTM(双通道) | 5.375 | 3.902 | 0.740 1 |
ConvLSTM+LSTM+MLP(双通道) | 5.412 | 3.952 | 0.736 0 |
ConvLSTM+LSTM(双通道) | 5.274 | 3.823 | 0.815 8 |
Tab. 3 Comparison of single-channel and dual-channel results
模型 | RMSE | MAE | R2 |
---|---|---|---|
LSTM(单通道)[ | 6.167 | 4.492 | 0.658 0 |
CNN(单通道)[ | 6.975 | 5.131 | 0.562 7 |
CNN+LSTM(双通道) | 5.375 | 3.902 | 0.740 1 |
ConvLSTM+LSTM+MLP(双通道) | 5.412 | 3.952 | 0.736 0 |
ConvLSTM+LSTM(双通道) | 5.274 | 3.823 | 0.815 8 |
模型 | RMSE | MAE | R2 |
---|---|---|---|
ConvLSTM+LSTM | 5.274 | 3.823 | 0.815 8 |
LSTM-ED | 5.715 | 4.211 | 0.706 1 |
XGBoost | 7.037 | 5.294 | 0.814 0 |
Tab. 4 Performance comparison of proposed model and benchmark models
模型 | RMSE | MAE | R2 |
---|---|---|---|
ConvLSTM+LSTM | 5.274 | 3.823 | 0.815 8 |
LSTM-ED | 5.715 | 4.211 | 0.706 1 |
XGBoost | 7.037 | 5.294 | 0.814 0 |
1 | SUN X, ANSARI N, WANG R. Optimizing resource utilization of a data center [J]. IEEE Communications Surveys & Tutorials, 2016, 18(4): 2822-2846. 10.1109/comst.2016.2558203 |
2 | PREVOST J, NAGOTHU K, KELLEY B, et al. Prediction of cloud data center networks loads using stochastic and neural models [C]// Proceedings of the 2011 6th International Conference on System of Systems Engineering. Piscataway: IEEE, 2011: 276-281. 10.1109/sysose.2011.5966610 |
3 | ZHANG Q, ZHANI M F, BOUTABA R, et al. HARMONY: dynamic heterogeneity-aware resource provisioning in the cloud [C]// Proceedings of the 2013 33rd International Conference on Distributed Computing Systems. Piscataway: IEEE, 2013: 510-519. 10.1109/icdcs.2013.28 |
4 | KUMAR T L M, SURENDRA H S, MUNIRAJAPPA R. Holt-winters exponential smoothing and sesonal ARIMA time-series technique for forecasting of onion price in Bangalore market [J]. Mysore Journal of Agricultural Sciences, 2011, 2(1): 602-607. |
5 | LI Q, HAO Q F, XIAO L M, et al. An Integrated approach to automatic management of virtualized resources in cloud environments [J]. Computer Journal, 2011, 54(6): 905-919. 10.1093/comjnl/bxq082 |
6 | SUN Y S, CHEN Y F, CHEN M C. A workload analysis of live event broadcast service in cloud [J]. Procedia Computer Science, 2013, 19(1): 1028-1033. 10.1016/j.procs.2013.06.143 |
7 | VERCAUTEREN T, AGGARWAL P, WANG X, et al. Hierarchical forecasting of web server workload using sequential Monte Carlo training [J]. IEEE Transactions on Signal Processing, 2007, 55(4): 1286-1297. 10.1109/tsp.2006.889401 |
8 | ARDAGNA D, CASOLARI S, COLAJANNI M, et al. Dual time-scale distributed capacity allocation and load redirect algorithms for cloud systems [J]. Journal of Parallel and Distributed Computing, 2012, 72(6): 796-808. 10.1016/j.jpdc.2012.02.014 |
9 | ROY N, DUBEY A, GOKHALE A. Efficient autoscaling in the cloud using predictive models for workload forecasting [C]// Proceedings of the 2011 International Conference on Cloud Computing. Piscataway: IEEE, 2011: 500-507. 10.1109/cloud.2011.42 |
10 | RAJARAM K, MALARVIZHI M P. Utilization based prediction model for resource provisioning [C]// Proceedings of the 2017 International Conference on Computer. Piscataway: IEEE, 2017:1-6. 10.1109/icccsp.2017.7944099 |
11 | KATTEPUR A, NAMBIAR M. Service demand modeling and performance prediction with single-user tests [J]. Performance Evaluation, 2017, 110(4): 1-21. 10.1016/j.peva.2017.02.003 |
12 | CAO L. Support vector machines experts for time series forecasting [J]. Neurocomputing, 2003, 51(4): 321-339. 10.1016/s0925-2312(02)00577-5 |
13 | EDDAHECH A, CHTOUROUS S, CHTOUROU M. Hierarchical neural networks based prediction and control of dynamic reconfiguration for multilevel embedded systems [J]. Journal of Systems Architecture, 2013, 59(1): 48-59. 10.1016/j.sysarc.2012.11.002 |
14 | DANG T, TRAN N, NGUYEN B M, et al. PD-GABP: A novel prediction model applying for elastic applications in distributed environment [C]// Proceedings of the 2016 3rd National Foundation for Science and Technology Development Conference on Information and Computer Science. Piscataway: IEEE, 2016: 240-248. 10.1109/nics.2016.7725658 |
15 | ZHANG G, PATUWO B E, HU M Y. Forecasting with artificial neural networks: the state of the art [J]. International Journal of Forecasting, 1998, 14(1): 35-62. |
16 | VENKATESAN. A genetic algorithm based artificial neural network model for the optimization of machining processes [J]. Neural Computing and Applications, 2009, 2(1): 135-140. |
17 | VAZQUEZ C. Time series forecasting of cloud data center workloads for dynamic resource provisioning [D]. San Antonio: The University of Texas at San Antonio, 2015: 10-15. |
18 | REN S, HE K, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149. 10.1109/tpami.2016.2577031 |
19 | 胡荣磊,芮璐,齐筱,等.基于循环神经网络和注意力模型的文本情感分析[J].计算机应用研究,2019,36(11):3282-3285. 10.19734/j.issn.1001-3695.2018.05.0300 |
HU R L, RUI L, QI X, et al. A novel approach to contextual sentiment analysis based on a neural network and attention model [J]. Application Research of Computer, 2019, 36(11): 3282-3285. 10.19734/j.issn.1001-3695.2018.05.0300 | |
20 | WANG Z, OATES T. Imaging time-series to improve classification and imputation [C]// Proceedings of the 2015 24th International Conference on Artificial Intelligence. Menlo Park: AAAI Press, 2015: 3939-3945. |
21 | NGUYEN H M, KALRA G, KIM D. Host load prediction in cloud computing using long short-term memory encoder-decoder [J]. Journal of supercomputing, 2019, 75(11): 7592-7605. 10.1007/s11227-019-02967-7 |
22 | CHEN T, GUESTRIN C. XGBoost: a scalable tree boosting system [EB/OL]. [2016-06-09]. . 10.1145/2939672.2939785 |
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