Load Prediction in Edge Computing Using Deep Auto-Regressive Recurrent Networks
Author:
Affiliation:
1. College of Computer and Data Science, Fuzhou University,China
2. School of Computing and Communications, University of Lancaster,Lancaster,UK
3. School of Engineering, Computing and Mathematics, University of Plymouth,Plymouth,UK
Funder
National Natural Science Foundation of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10278505/10278554/10278924.pdf?arnumber=10278924
Reference18 articles.
1. A novel approach to workload prediction using attention-based LSTM encoder-decoder network in cloud environment
2. An Efficient Deep Learning Model to Predict Cloud Workload for Industry Informatics
3. BHyPreC: A Novel Bi-LSTM Based Hybrid Recurrent Neural Network Model to Predict the CPU Workload of Cloud Virtual Machine
4. Towards Accurate Prediction for High-Dimensional and Highly-Variable Cloud Workloads with Deep Learning
5. An intelligent regressive ensemble approach for predicting resource usage in cloud computing
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