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Tr-Predictior: An Ensemble Transfer Learning Model for Small-Sample Cloud Workload Prediction
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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9778472/ https://www.ncbi.nlm.nih.gov/pubmed/36554175 http://dx.doi.org/10.3390/e24121770 |
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author | Liu, Chunhong Jiao, Jie Li, Weili Wang, Jingxiong Zhang, Junna |
author_facet | Liu, Chunhong Jiao, Jie Li, Weili Wang, Jingxiong Zhang, Junna |
author_sort | Liu, Chunhong |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9778472 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-97784722022-12-23 Tr-Predictior: An Ensemble Transfer Learning Model for Small-Sample Cloud Workload Prediction Liu, Chunhong Jiao, Jie Li, Weili Wang, Jingxiong Zhang, Junna Entropy (Basel) Article 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. MDPI 2022-12-03 /pmc/articles/PMC9778472/ /pubmed/36554175 http://dx.doi.org/10.3390/e24121770 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Liu, Chunhong Jiao, Jie Li, Weili Wang, Jingxiong Zhang, Junna Tr-Predictior: An Ensemble Transfer Learning Model for Small-Sample Cloud Workload Prediction |
title | Tr-Predictior: An Ensemble Transfer Learning Model for Small-Sample Cloud Workload Prediction |
title_full | Tr-Predictior: An Ensemble Transfer Learning Model for Small-Sample Cloud Workload Prediction |
title_fullStr | Tr-Predictior: An Ensemble Transfer Learning Model for Small-Sample Cloud Workload Prediction |
title_full_unstemmed | Tr-Predictior: An Ensemble Transfer Learning Model for Small-Sample Cloud Workload Prediction |
title_short | Tr-Predictior: An Ensemble Transfer Learning Model for Small-Sample Cloud Workload Prediction |
title_sort | tr-predictior: an ensemble transfer learning model for small-sample cloud workload prediction |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9778472/ https://www.ncbi.nlm.nih.gov/pubmed/36554175 http://dx.doi.org/10.3390/e24121770 |
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