Neural Networks in the Real World: Overcoming the  Challenges of Deployment

Dr.K.Muthukannan,Mr.E.Sivarajan,K.P.Vijayalakshmi,MS.Pavithra P M

Neural Networks in the Real World: Overcoming the Challenges of Deployment

Authors

Dr.K.Muthukannan,Mr.E.Sivarajan,K.P.Vijayalakshmi,MS.Pavithra P M

Keywords:

performing traffic steering/mirroring, human intervention, including smart network telemetry, smart traffic engineering, real-time flow classification, and network tomography

Abstract

The quest for self-driving networks poses growing pressure to manage network eventsat a nano-second scale. In this article, we make a case for leveraging programmable forwardingplanes to achieve self-driving networks and respond to their dynamism in real time by networkintelligence and without performing traffic

References

. J. Zerwas et al., “Netboa: Self-Driving Network Benchmarking,” Proc. Wksp. Network

Meets AI & ML, ACM, 2023, pp. 8–14.

. G. Siracusano and R. Bifulco, “In-Network Neural Networks,” CoRR, vol. abs/1801.05731,

; http://arxiv.org/abs/1801.05731.

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Published

2024-08-14

How to Cite

Dr.K.Muthukannan,Mr.E.Sivarajan,K.P.Vijayalakshmi,MS.Pavithra P M. (2024). Neural Networks in the Real World: Overcoming the Challenges of Deployment. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 3330–3336. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/2477