A neural gas network-based scheme for SDN many-field packet classification
Author:
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/s11227-023-05564-x.pdf
Reference31 articles.
1. Alimohammadi H, Ahmadi M (2019) Clustering-based many-field packet classification in software-defined networking. J Netw Comput Appl 147(102):428. https://doi.org/10.1016/j.jnca.2019.102428
2. Alimohammadi H, Ahmadi M (2020) Common non-wildcard portion-based partitioning approach to SDN many-field packet classification. Comput Netw 181(107):534. https://doi.org/10.1016/j.comnet.2020.107534
3. Bosshart P, Gibb G, Kim HS, Varghese G, McKeown N, Izzard M, Mujica F, Horowitz M (2013) Forwarding metamorphosis: Fast programmable match-action processing in hardware for SDN. In: Proceedings of the ACM SIGCOMM 2013 Conference on SIGCOMM, Association for Computing Machinery, New York, NY, USA, SIGCOMM ’13, p 99-110, https://doi.org/10.1145/2486001.2486011
4. Chang YK, Hsueh CS (2016) Range-enhanced packet classification design on FPGA. IEEE Trans Emerg Top Comput 4(2):214–224. https://doi.org/10.1109/TETC.2015.2449666
5. Congdon PT, Mohapatra P, Farrens M, Akella V (2014) Simultaneously reducing latency and power consumption in openflow switches. IEEE/ACM Trans Netw 22(3):1007–1020. https://doi.org/10.1109/TNET.2013.2270436
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