Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Liu, Chunhong, Jiao, Jie, Li, Weili, Wang, Jingxiong, Zhang, Junna
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
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
_version_ 1784856370011439104
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
work_keys_str_mv AT liuchunhong trpredictioranensembletransferlearningmodelforsmallsamplecloudworkloadprediction
AT jiaojie trpredictioranensembletransferlearningmodelforsmallsamplecloudworkloadprediction
AT liweili trpredictioranensembletransferlearningmodelforsmallsamplecloudworkloadprediction
AT wangjingxiong trpredictioranensembletransferlearningmodelforsmallsamplecloudworkloadprediction
AT zhangjunna trpredictioranensembletransferlearningmodelforsmallsamplecloudworkloadprediction