A novel approach to workload prediction using attention-based LSTM encoder-decoder network in cloud...
A novel approach to workload prediction using attention-based LSTM encoder-decoder network in cloud environment
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Publisher
Cham: Springer International Publishing
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Language
English
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Cham: Springer International Publishing
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Server workload in the form of cloud-end clusters is a key factor in server maintenance and task scheduling. How to balance and optimize hardware resources and computation resources should thus receive more attention. However, we have observed that the disordered execution of running application and batching seriously cuts down the efficiency of th...
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Full title
A novel approach to workload prediction using attention-based LSTM encoder-decoder network in cloud environment
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TN_cdi_doaj_primary_oai_doaj_org_article_91e13d4c46f24938bb42e7d595d2dee5
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_91e13d4c46f24938bb42e7d595d2dee5
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ISSN
1687-1499,1687-1472
E-ISSN
1687-1499
DOI
10.1186/s13638-019-1605-z
