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Title

AI/ML Models for NFV Usecases

Status

Status
colourBlueGreen
titlePENDING TSC REVIEWApproved

Difficulty

Status
colourRed
titleHigh


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  1. VNF/CNF Placement
  2. VNF/CNF resource/performance/failure prediction

  3. Packet-Loss Classification

The overall architecture of the project can be depicted as below:

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ProblemModelLinkComments

Prediction of VNF Resource Demands

RNN, LSTM

https://ieeexplore.ieee.org/document/8806620
VNF PlacementNeural Network Model (MLP)

https://ieeexplore.ieee.org/document/8806631

https://arxiv.org/abs/2001.07787


Additional Information

LFN Acumos: https://www.acumos.org/

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Collectd : https://collectd.org/

Hosting Repo: As a Subproject under CIRV - https://github.com/opnfv/cirv

Learning Objectives

ML Techniques: Deep_learning.

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Spirent Communications

sridhar.rao@spirent.com 


Volunteers

Girish L.  (PhD Student)

VTU

girishlingappa7@gmail.com