eBPF and XDP Technologies as Enablers for Ultra-Fast and Programmable Next-Gen Network Infrastructures

Author:

Gallego-Madrid JorgeORCID,Sanchez-Iborra RamonORCID,Gomez Antonio SkarmetaORCID

Publisher

Springer Nature Singapore

Reference14 articles.

1. Ali, R., Zikria, Y.B., Bashir, A.K., Garg, S., Kim, H.S.: URLLC for 5G and beyond: requirements, enabling incumbent technologies and network intelligence. IEEE Access 9, 67064–67095 (2021). https://doi.org/10.1109/ACCESS.2021.3073806, https://ieeexplore.ieee.org/document/9406015/

2. Bachl, M., Fabini, J., Zseby, T.: A Flow-Based IDS Using Machine Learning in eBPF (2021). http://arxiv.org/abs/2102.09980

3. Hauser, F., Häberle, M., Merling, D., Lindner, S., Gurevich, V., Zeiger, F., Frank, R., Menth, M.: A survey on data plane programming with P4: fundamentals. Adv. Appl. Res. (2021). http://arxiv.org/abs/2101.10632

4. Henry, J., Szigeti, T., Contreras, L.: Diffserv to QCI mapping. In: RFC Internet-Draft, pp. 1–43 (2020). https://tools.ietf.org/pdf/draft-henry-tsvwg-diffserv-to-qci-04.pdf

5. Hong, J., Jeong, S., Yoo, J.H., Hong, J.W.K.: Design and implementation of eBPF-based virtual TAP for inter-VM traffic monitoring. In: 1st International Workshop on High-Precision Networks Operations and Control (HiPNet 2018). 14th International Conference on Network and Service Management (CNSM), pp. 402–407. Italy, Rome (2018)

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