Input Feature Engineering for Lstm-Based Vnf Resource Usage Prediction

Author:

St-Onge Cédric,Kara Nadjia,Edstrom Claes

Publisher

Elsevier BV

Subject

General Earth and Planetary Sciences,General Environmental Science

Reference23 articles.

1. Managing Virtualized Networks and Services with Machine Learning;R Boutaba;Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning,2021

2. Topology-Aware Prediction of Virtual Network Function Resource Requirements;R Mijumbi;IEEE Transactions on Network and Service Management,2017

3. On Performance VNF Load Prediction Models in Service Function Chaining;Y Cho;International Conference on ICT Convergence,2020

4. A comparative for Time Series Forecasting within software 5G networks;P Chakraborty;2020 14th International Conference on Signal Processing and Communication Systems, ICSPCS 2020 -Proceedings,2020

5. Application of a Long Short Term Memory neural predictor with asymmetric loss function for the resource allocation in NFV network architectures;V Eramo;Computer Networks,2020

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Graph Transformer and LSTM Attention for VNF Multi-Step Workload Prediction in SFC;IEEE Transactions on Network and Service Management;2024-08

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