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
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