Through the RTT Lens : A Comparative study of CUBIC and BBR Congestion Control
DOI:
https://doi.org//10.32628/CSEIT2390652Keywords:
ALU, Adders, Subtractors, BorrowAbstract
As the demand for high-performance internet applications continues to surge, the efficiency of network congestion control algorithms becomes a critical factor in ensuring a seamless user experience. This research paper delves into the comparative analysis of two prominent congestion control mechanisms: BBR (Bottleneck Bandwidth and Round- trip propagation time) and Cubic[1]. Both algorithms play pivotal roles in regulating data flow within networks, but their approaches differ significantly. The comparison section highlights the adaptive behavior of each algorithm, emphasizing real-world implications for diverse network scenarios. The discussion interprets the findings, offering a nuanced understanding of the strengths and weaknesses of both BBR and Cubic, thereby contributing to the broader discourse on congestion control strategies.
References
- N. Cardwell, “BBR: Congestion-Based Congestion Control.” [Online]. Available: https://research.google/pubs/pub45646/
- Larry L. Peterson, Bruce S. Davie, “Computer Networks (Fifth Edition), 2012.”
- M. Geist, “Overview of TCP Congestion Control Algorithms.” [Online]. Available: https://www.net.in.tum.de/fileadmin/TUM/NE T/NET-2019-06-1/NET-2019-06-1_03.pdf
- S. H. Low, F. Paganini, and J. C. Doyle, “‘Internet congestion control,’ in IEEE Control Systems Magazine, vol. 22, no. 1, pp. 28-43, , doi: 10.1109/37.980245.,” 2002.
- Locating Internet Bottlenecks: Algorithms, Measurements, and Implications. (2004). United States: Carnegie- mellon univ pittsburgh pa school of computer Science.
- D. Scholz, B. Jaeger, L. Schwaighofer, D. Raumer, F. Geyer, and G. Carle, “‘Towards a Deeper Understanding of TCP BBR Congestion Control,’ 2018 IFIP Networking Conference (IFIP Networking) and Workshops, Zurich, Switzerland, 2018, pp. 1-9, doi: 10.23919/IFIPNetworking.2018.8696830.”
- T. V. Lakshman and U. Madhow, “‘The performance of TCP/IP for networks with high bandwidth-delay products and random loss,’ in IEEE/ACM Transactions on Networking, vol. 5, no. 3, pp. 336-350, June 1997, doi: 10.1109/90.611099.”
- C. Williamson and D. Cheriton, “Loss-Load Curves: Support for Rate-Based Congestion Control in High-Speed Datagram Networks”.
- M. Hock, R. Bless, and M. Zitterbart, “M. Hock, R. Bless and M. Zitterbart, ‘Experimental evaluation of BBR congestion control,’ 2017 IEEE 25th International Conference on Network Protocols (ICNP), Toronto, ON, Canada, 2017, pp. 1-10, doi: 10.1109/ICNP.2017.8117540.”.
- V. Sharma and S. Poojary, “‘Analytical Model for Congestion Control and Throughput with TCP CUBIC Connections,’ 2011 IEEE Global Telecommunications Conference - GLOBECOM 2011, Houston, TX, USA, 2011, pp. 1-6, doi: 10.1109/GLOCOM.2011.6134001.”. [11] C.-H. Hsu and U. Kremer, “IPERF : A Framework for Automatic Construction of Performance Prediction Models”.
- “The Tcpdump Group. "tcpdump - a powerful tool for network monitoring and data acquisition.” [Online]. Available: https://www.tcpdump.org/
- N. Cardwell, Y. Cheng, and C. S. Gunn, “BBR congestion control: an update.,” Mar. 2017. [14] A. Mishra, T. W. Han, and B. Leong, “Are we heading towards a BBR-dominant internet?,” Oct. 2022.
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