Tr-Predictior: An Ensemble Transfer Learning Model for Small-Sample Cloud Workload Prediction

被引:5
|
作者
Liu, Chunhong [1 ,2 ]
Jiao, Jie [1 ]
Li, Weili [1 ]
Wang, Jingxiong [1 ]
Zhang, Junna [1 ,2 ]
机构
[1] Henan Normal Univ, Coll Comp & Informat Engn, Xinxiang 453007, Peoples R China
[2] Engn Lab Intelligence Business, Xinxiang 453007, Peoples R China
基金
中国国家自然科学基金;
关键词
cloud data center; transfer entropy; workload forecast; ensemble learning; transfer learning; NEURAL-NETWORKS; FRAMEWORK; ERROR;
D O I
10.3390/e24121770
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
Accurate workload prediction plays a key role in intelligent scheduling decisions on cloud platforms. There are massive amounts of short-workload sequences in the cloud platform, and the small amount of data and the presence of outliers make accurate workload sequence prediction a challenge. For the above issues, this paper proposes an ensemble learning method based on sample weight transfer and long short-term memory (LSTM), termed as Tr-Predictor. Specifically, a selection method of similar sequences combining time warp edit distance (TWED) and transfer entropy (TE) is proposed to select a source domain dataset with higher similarity for the target workload sequence. Then, we upgrade the basic learner of the ensemble model two-stage TrAdaBoost.R2 to LSTM in the deep model and enhance the ability of the ensemble model to extract sequence features. To optimize the weight adjustment strategy, we adopt a two-stage weight adjustment strategy and select the best weight for the learner according to the sample error and model error. Finally, the above process determines the parameters of the target model and uses the target model to predict the short-task sequences. In the experimental validation, we arbitrarily select nine sets of short-workload data from the Google dataset and three sets of short-workload data from the Alibaba cluster to verify the prediction effectiveness of the proposed algorithm. The experimental results show that compared with the commonly used cloud workload prediction methods Tr-Predictor has higher prediction accuracy on the small-sample workload. The prediction indicators of the ablation experiments show the performance gain of each part in the proposed method.
引用
收藏
页数:17
相关论文
共 50 条
  • [31] Small-sample day-ahead power load forecasting of integrated energy system based on feature transfer learning
    Sun X.-Y.
    Li J.-Z.
    Zeng B.
    Gong D.-W.
    Lian Z.-Y.
    Kongzhi Lilun Yu Yingyong/Control Theory and Applications, 2021, 38 (01): : 63 - 72
  • [32] Small-Sample Short-Term Photovoltaic Output Prediction Model Based on GRA-SSA-GNNM Method
    Wang, Qi
    Mutailipu, Meiheriayi
    Xiong, Qiang
    Jing, Xuehui
    Yang, Yande
    PROCESSES, 2024, 12 (11)
  • [33] Pre-trained network-based transfer learning: A small-sample machine learning approach to nuclear power plant classification problem
    Zhong, Xianping
    Ban, Heng
    ANNALS OF NUCLEAR ENERGY, 2022, 175
  • [34] Improved Deep Transfer Learning Model for Scarce Sample Kechuang 50 Prediction
    Xu, Bo
    Tu, Wenwen
    2024 9TH INTERNATIONAL CONFERENCE ON COMPUTER AND COMMUNICATION SYSTEMS, ICCCS 2024, 2024, : 1216 - 1221
  • [35] A Novel Hybrid Transfer Learning Approach for Small-Sample High-Voltage Circuit Breaker Fault Diagnosis on-Site
    Wang, Yanxin
    Yan, Jing
    Wang, Jianhua
    Geng, Yingsan
    IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, 2023, 59 (04) : 4942 - 4950
  • [36] A Cross-project Defect Prediction Model Using Feature Transfer and Ensemble Learning
    Zeng, Fuping
    Lin, Wanting
    Xing, Ying
    Sun, Lu
    Yang, Bin
    TEHNICKI VJESNIK-TECHNICAL GAZETTE, 2022, 29 (04): : 1089 - 1099
  • [37] E2LG: a multiscale ensemble of LSTM/GAN deep learning architecture for multistep-ahead cloud workload prediction
    Peyman Yazdanian
    Saeed Sharifian
    The Journal of Supercomputing, 2021, 77 : 11052 - 11082
  • [38] E2LG: a multiscale ensemble of LSTM/GAN deep learning architecture for multistep-ahead cloud workload prediction
    Yazdanian, Peyman
    Sharifian, Saeed
    JOURNAL OF SUPERCOMPUTING, 2021, 77 (10): : 11052 - 11082
  • [39] A tree-like Bayesian structure learning algorithm for small-sample datasets from complex biological model systems
    Yin, Weiwei
    Garimalla, Swetha
    Moreno, Alberto
    Galinski, Mary R.
    Styczynski, Mark P.
    BMC SYSTEMS BIOLOGY, 2015, 9
  • [40] Toxicity prediction and classification of Gunqile-7 with small sample based on transfer learning method
    Zhao H.
    Qiu S.
    Bai M.
    Wang L.
    Wang Z.
    Computers in Biology and Medicine, 2024, 173