Evaluating L4S Framework Performance with Programmable Data Plane Hardware
Resumo
L4S, which stands for Low Latency, Low Loss, Scalable Throughput, is an initiative within the Internet Engineering Task Force (IETF) dedicated to enhancing the performance of real-time interactive applications across the Internet, like online gaming, video conferencing, and virtual reality. Its core principles revolve around implementing a novel variant of congestion control (CC) algorithm on end hosts and deploying an Active Queue Management (AQM) scheme on network nodes. Evaluating AQM algorithm adherence within the L4S framework on conventional network devices is challenging due to limited access to implementation details. However, the game changed with the introduction of data plane programmability, which facilitates the incorporation of intelligence during packet processing at the hardware’s most proximate level, without the necessity for control plane intervention. In this context, this paper evaluates the implementation of two key L4S-capable algorithms, iRED and PI2, using a real hardware P4-capable switch (Tofino2). The evaluation aims to verify the adherence of these algorithms to the L4S framework and observe their coexistence with non-L4S flows. Through controlled variations in bandwidth and delay, we assess the “goodput” metric to understand under what conditions iRED and PI2 demonstrate enhanced fairness.Referências
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de Almeida, L. C., Matos, G., Pasquini, R., Papagianni, C., and Verdi, F. L. (2022). iRED: Improving the DASH QoS by dropping packets in programmable data planes. In 2022 18th International Conference on Network and Service Management (CNSM), pages 136–144.
De Schepper, K., Bondarenko, O., Tsang, I.-J., and Briscoe, B. (2016). PI2: A Linearized AQM for Both Classic and Scalable TCP. In Proceedings of the 12th International on Conference on Emerging Networking EXperiments and Technologies, CoNEXT ’16, page 105–119, New York, NY, USA. Association for Computing Machinery.
De Schepper, K., Briscoe, B., and White, G. (2023). Dual-Queue Coupled Active Queue Management (AQM) for Low Latency, Low Loss, and Scalable Throughput (L4S). RFC 9332.
Floyd, S. and Jacobson, V. (1993). Random early detection gateways for congestion avoidance. IEEE/ACM Transactions on Networking, 1(4):397–413.
Gombos, G., Mouw, M., Laki, S., Papagianni, C., and De Schepper, K. (2022). Active Queue Management on the Tofino programmable switch: The (Dual)PI2 case. In ICC 2022 - IEEE International Conference on Communications, pages 1685–1691.
Jacobson, V. (1988). Congestion Avoidance and Control. In Symposium Proceedings on Communications Architectures and Protocols, SIGCOMM ’88, page 314–329, New York, NY, USA. Association for Computing Machinery.
Ky, J. R., Graff, P., Mathieu, B., and Cholez, T. (2023). A Hybrid P4/NFV Architecture for Cloud Gaming Traffic Detection with Unsupervised ML. In 2023 IEEE Symposium on Computers and Communications (ISCC), pages 733–738.
Malangadan, N., Raina, G., and Ghosh, D. (2023). Synchronisation in TCP networks with Drop-Tail Queues. In Networking and Internet Architecture (cs.NI); Dynamical Systems (math.DS). arXiv.
Nguyen, H. N., Mathieu, B., Letourneau, M., Doyen, G., Tuffin, S., and Oca, E. M. d. (2023). A Comprehensive P4-based Monitoring Framework for L4S leveraging In-band Network Telemetry. In NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium, pages 1–6.
Peterson, L., Brakmo, L., and Davie, B. (2022). TCP Congestion Control: A Systems Approach. Systems Approach.
Srivastava, A., Fund, F., and Panwar, S. S. (2022). Coexistence of delay-based TCP congestion control: Challenges and opportunities. In 2022 IEEE International Workshop Technical Committee on Communications Quality and Reliability (CQR), pages 43–48.
Alli-Oke, R. O. (2022). On the validity of numerical simulations for control-theoretic AQM schemes in computer networks. Mathematics and Computers in Simulation, 193:466–480.
Barefoot/Intel (2021). P416 Intel Tofino™ Native Architecture – Public Version.
BoruOljira, D., Grinnemo, K.-J., Brunstrom, A., and Taheri, J. (2020). Validating the Sharing Behavior and Latency Characteristics of the L4S Architecture. SIGCOMM Comput. Commun. Rev., 50(2):37–44.
Briscoe, B., De Schepper, K., Bagnulo, M., and White, G. (2023). RFC 9330: Low Latency, Low Loss, and Scalable Throughput (L4S) Internet Service: Architecture.
Briscoe, B., Schepper, K. D., Albisser, O., Tilmans, O., Kuhn, N., Fairhurst, G., Scheffenegger, R., Abrahamsson, M., Johansson, I., Balasubramanian, P., Pullen, D., Bracha, G., Morton, J., and Täht, D. (2018). Implementing the ’ Prague Requirements ’ for Low Latency Low Loss Scalable Throughput ( L 4 S ). In Netdev 0x13, THE Technical Conference on Linux Networking.
Busse-Grawitz, C., Meier, R., Dietmüller, A., Bühler, T., and Vanbever, L. (2019). pForest: In-Network Inference with Random Forests. CoRR, abs/1909.05680.
Chen, X., Feibish, S. L., Koral, Y., Rexford, J., Rottenstreich, O., Monetti, S. A., and Wang, T.-Y. (2019). Fine-Grained Queue Measurement in the Data Plane. In Proceedings of the 15th International Conference on Emerging Networking Experiments And Technologies, CoNEXT ’19, page 15–29, New York, NY, USA. Association for Computing Machinery.
de Almeida, L. C., Matos, G., Pasquini, R., Papagianni, C., and Verdi, F. L. (2022). iRED: Improving the DASH QoS by dropping packets in programmable data planes. In 2022 18th International Conference on Network and Service Management (CNSM), pages 136–144.
De Schepper, K., Bondarenko, O., Tsang, I.-J., and Briscoe, B. (2016). PI2: A Linearized AQM for Both Classic and Scalable TCP. In Proceedings of the 12th International on Conference on Emerging Networking EXperiments and Technologies, CoNEXT ’16, page 105–119, New York, NY, USA. Association for Computing Machinery.
De Schepper, K., Briscoe, B., and White, G. (2023). Dual-Queue Coupled Active Queue Management (AQM) for Low Latency, Low Loss, and Scalable Throughput (L4S). RFC 9332.
Floyd, S. and Jacobson, V. (1993). Random early detection gateways for congestion avoidance. IEEE/ACM Transactions on Networking, 1(4):397–413.
Gombos, G., Mouw, M., Laki, S., Papagianni, C., and De Schepper, K. (2022). Active Queue Management on the Tofino programmable switch: The (Dual)PI2 case. In ICC 2022 - IEEE International Conference on Communications, pages 1685–1691.
Jacobson, V. (1988). Congestion Avoidance and Control. In Symposium Proceedings on Communications Architectures and Protocols, SIGCOMM ’88, page 314–329, New York, NY, USA. Association for Computing Machinery.
Ky, J. R., Graff, P., Mathieu, B., and Cholez, T. (2023). A Hybrid P4/NFV Architecture for Cloud Gaming Traffic Detection with Unsupervised ML. In 2023 IEEE Symposium on Computers and Communications (ISCC), pages 733–738.
Malangadan, N., Raina, G., and Ghosh, D. (2023). Synchronisation in TCP networks with Drop-Tail Queues. In Networking and Internet Architecture (cs.NI); Dynamical Systems (math.DS). arXiv.
Nguyen, H. N., Mathieu, B., Letourneau, M., Doyen, G., Tuffin, S., and Oca, E. M. d. (2023). A Comprehensive P4-based Monitoring Framework for L4S leveraging In-band Network Telemetry. In NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium, pages 1–6.
Peterson, L., Brakmo, L., and Davie, B. (2022). TCP Congestion Control: A Systems Approach. Systems Approach.
Srivastava, A., Fund, F., and Panwar, S. S. (2022). Coexistence of delay-based TCP congestion control: Challenges and opportunities. In 2022 IEEE International Workshop Technical Committee on Communications Quality and Reliability (CQR), pages 43–48.
Publicado
24/05/2024
Como Citar
ALMEIDA, Leandro C. de; MACIEL JR., Paulo Ditarso; VERDI, Fábio L..
Evaluating L4S Framework Performance with Programmable Data Plane Hardware. In: WORKSHOP DE GERÊNCIA E OPERAÇÃO DE REDES E SERVIÇOS (WGRS), 29. , 2024, Niterói/RJ.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 196-209.
ISSN 2595-2722.
DOI: https://doi.org/10.5753/wgrs.2024.3286.