Matches in SemOpenAlex for { <https://semopenalex.org/work/W4320057435> ?p ?o ?g. }
Showing items 1 to 75 of
75
with 100 items per page.
- W4320057435 abstract "The load prediction of host resources is a key issue to enhance the cloud computing aid allocation system. With the change of cloud computing resource load displaying extra and extra complicated characteristics, traditional prediction algorithms can solely predict the linear traits of data, and it is tough to precisely predict useful resource usage. In order to enhance the forecasting accuracy of the model, a blended load forecasting algorithm based totally on machine learning is proposed. The machine learning prediction model can nicely match the nonlinear traits of the data. The linear phase of the algorithm makes use of ARIMA prediction, and the nonlinear section makes use of particle swarm optimization algorithm to optimize LSTM prediction. Then, the optimal least squares method is used to redistribute the prediction error weights of the autoregressive differential moving average model (ARIMA) and the long-term and short-term memory network models (LSTM), and finally the prediction results are output. The comparison experiment is carried out with the open actual load data set. The experimental results show that the prediction accuracy of the weight redistribution combination model is significantly higher than that of other traditional prediction models and machine learning prediction models when the prediction time efficiency is similar, and the real-time prediction error of resource load in cloud environment is significantly reduced." @default.
- W4320057435 created "2023-02-12" @default.
- W4320057435 creator A5020670079 @default.
- W4320057435 creator A5037592426 @default.
- W4320057435 creator A5040298216 @default.
- W4320057435 creator A5055729372 @default.
- W4320057435 date "2022-11-25" @default.
- W4320057435 modified "2023-09-29" @default.
- W4320057435 title "Load prediction optimization based on machine learning in cloud computing environment" @default.
- W4320057435 cites W1984255960 @default.
- W4320057435 cites W2107240614 @default.
- W4320057435 cites W2891974069 @default.
- W4320057435 cites W2899427376 @default.
- W4320057435 cites W2942032779 @default.
- W4320057435 cites W3008629209 @default.
- W4320057435 cites W3121151732 @default.
- W4320057435 cites W4210875041 @default.
- W4320057435 cites W4210912962 @default.
- W4320057435 doi "https://doi.org/10.1145/3573834.3574511" @default.
- W4320057435 hasPublicationYear "2022" @default.
- W4320057435 type Work @default.
- W4320057435 citedByCount "0" @default.
- W4320057435 crossrefType "proceedings-article" @default.
- W4320057435 hasAuthorship W4320057435A5020670079 @default.
- W4320057435 hasAuthorship W4320057435A5037592426 @default.
- W4320057435 hasAuthorship W4320057435A5040298216 @default.
- W4320057435 hasAuthorship W4320057435A5055729372 @default.
- W4320057435 hasConcept C111919701 @default.
- W4320057435 hasConcept C11413529 @default.
- W4320057435 hasConcept C119857082 @default.
- W4320057435 hasConcept C121332964 @default.
- W4320057435 hasConcept C124101348 @default.
- W4320057435 hasConcept C150217764 @default.
- W4320057435 hasConcept C151406439 @default.
- W4320057435 hasConcept C154945302 @default.
- W4320057435 hasConcept C158622935 @default.
- W4320057435 hasConcept C24338571 @default.
- W4320057435 hasConcept C41008148 @default.
- W4320057435 hasConcept C45804977 @default.
- W4320057435 hasConcept C50644808 @default.
- W4320057435 hasConcept C62520636 @default.
- W4320057435 hasConcept C79974875 @default.
- W4320057435 hasConcept C85617194 @default.
- W4320057435 hasConceptScore W4320057435C111919701 @default.
- W4320057435 hasConceptScore W4320057435C11413529 @default.
- W4320057435 hasConceptScore W4320057435C119857082 @default.
- W4320057435 hasConceptScore W4320057435C121332964 @default.
- W4320057435 hasConceptScore W4320057435C124101348 @default.
- W4320057435 hasConceptScore W4320057435C150217764 @default.
- W4320057435 hasConceptScore W4320057435C151406439 @default.
- W4320057435 hasConceptScore W4320057435C154945302 @default.
- W4320057435 hasConceptScore W4320057435C158622935 @default.
- W4320057435 hasConceptScore W4320057435C24338571 @default.
- W4320057435 hasConceptScore W4320057435C41008148 @default.
- W4320057435 hasConceptScore W4320057435C45804977 @default.
- W4320057435 hasConceptScore W4320057435C50644808 @default.
- W4320057435 hasConceptScore W4320057435C62520636 @default.
- W4320057435 hasConceptScore W4320057435C79974875 @default.
- W4320057435 hasConceptScore W4320057435C85617194 @default.
- W4320057435 hasLocation W43200574351 @default.
- W4320057435 hasOpenAccess W4320057435 @default.
- W4320057435 hasPrimaryLocation W43200574351 @default.
- W4320057435 hasRelatedWork W2052669361 @default.
- W4320057435 hasRelatedWork W2305568609 @default.
- W4320057435 hasRelatedWork W2889516516 @default.
- W4320057435 hasRelatedWork W2889626453 @default.
- W4320057435 hasRelatedWork W3128744131 @default.
- W4320057435 hasRelatedWork W3186873483 @default.
- W4320057435 hasRelatedWork W3209157187 @default.
- W4320057435 hasRelatedWork W4207046107 @default.
- W4320057435 hasRelatedWork W4321460289 @default.
- W4320057435 hasRelatedWork W4323316459 @default.
- W4320057435 isParatext "false" @default.
- W4320057435 isRetracted "false" @default.
- W4320057435 workType "article" @default.