Multivariate outlier filtering for A-NFVLearn: an advanced deep VNF resource usage forecasting technique-Reference-Cited by-同舟云学术

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Multivariate outlier filtering for A-NFVLearn: an advanced deep VNF resource usage forecasting technique

Published:2023-04-26 Issue:14 Volume:79 Page:16206-16232
ISSN:0920-8542
Container-title:The Journal of Supercomputing
language:en
Short-container-title:J Supercomput

Author:

St-Onge Cédric,Kara Nadjia,Edstrom Claes

Funder

Natural Sciences and Engineering Research Council of Canada

Publisher

Springer Science and Business Media LLC

Subject

Hardware and Architecture,Information Systems,Theoretical Computer Science,Software

Link

https://link.springer.com/content/pdf/10.1007/s11227-023-05283-3.pdf

Reference26 articles.

1. Boutaba R, Shahriar N, Salahuddin MA, Limam N (2021) Managing virtualized networks and services with machine learning. Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning 33–68

2. St-Onge C, Kara N, Edstrom C (2022) Nfvlearn: A multi-resource, long short-term memory-based virtual network function resource usage prediction architecture. Practice and Experience, Software. https://doi.org/10.1002/spe.3160

3. Mijumbi R, Hasija S, Davy S, Davy A, Jennings B, Boutaba R (2017) Topology-aware prediction of virtual network function resource requirements. IEEE Trans Netw Serv Manag 14:106–120

4. Cho Y, Jang S, Pack S (2020) On performance VNF load prediction models in service function chaining. In: International Conference on ICT Convergence, vol 2020-Octob, pp 344–346

5. Afanasyev DO, Fedorova EA (2019) On the impact of outlier filtering on the electricity price forecasting accuracy. Appl Energy 236:196–210

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