Plausibility of BBR as CUBIC’S Replacement and proposed improvement to BBR using GENET

Authors

  • Dr. Kunwar Asif  Department of Computer Science, New Jersey Institute of Technology, New Jersey, USA
  • Hemanjali Kadali  Department of Computer Science, New Jersey Institute of Technology, New Jersey, USA
  • Sathwik Varma Mudduluri  Department of Computer Science, New Jersey Institute of Technology, New Jersey, USA
  • Pawan Sai Krishna Reddy Kerelly  Department of Computer Science, New Jersey Institute of Technology, New Jersey, USA

DOI:

https://doi.org/10.32628/CSEIT2390660

Keywords:

Bottleneck Bandwidth and Round-trip propagation time algorithm, congestion control algorithms, GENET's

Abstract

Recent advancements in deep reinforcement learning (RL) have opened new avenues for enhancing network congestion control algorithms. Our research builds upon these developments, particularly focusing on the BBR (Bottleneck Bandwidth and Round-trip propagation time) congestion control algorithm. We propose integrating GENET's reinforcement learning framework, a novel training paradigm that has demonstrated success in various network adaptation algorithms, including adaptive video streaming, congestion control, and load balancing. GENET leverages curriculum learning to effectively train RL models by progressively introducing more challenging network environments. This method counters the common pitfalls in RL training, such as suboptimal performance in a wide range of environments and poor generalization in narrowly defined training scenarios. Our approach exploits the strengths of GENET in identifying and emphasizing network conditions where the current RL model underperforms compared to traditional rule-based baselines, thereby facilitating significant improvements. This research aims to demonstrate that applying GENET's methodology to the BBR congestion control algorithm can yield RL policies that surpass both regularly trained RL policies and conventional baselines, thereby advancing the efficiency and reliability of network congestion control.

References

  1. Cardwell, N., Cheng, Y., Gunn, C. S., Yeganeh, S. H., & Jacobson, V. (2016). BBR: Congestion-Based Congestion Control. Communications of the ACM, 60(2), 58-66.
  2. Zhengxu Xia, Yajie Zhou, Francis Y. Yan, Junchen Jiang. (2022). GENET: Evolving Reinforcement Learning Algorithms for Adaptive Network Control.
  3. Jacobson, V. (1988). Congestion avoidance and control. In Proceedings of SIGCOMM '88, Symposium on Communications Architectures and Protocols, 314-329.
  4. Floyd, S., & Henderson, T. (1999). The NewReno Modification to TCP's Fast Recovery Algorithm. RFC 2582.
  5. Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press.
  6. Mnih, V., Kavukcuoglu, K., Silver, D., et al. (2015). Human-level control through deep reinforcement learning. Nature, 518, 529-533.
  7. Ha, S., Rhee, I., & Xu, L. (2008). CUBIC: A New TCP-Friendly High-Speed TCP Variant. ACM SIGOPS Operating Systems Review, 42(5), 64-74.

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Published

2023-12-30

Issue

Section

Research Articles

How to Cite

[1]
Dr. Kunwar Asif, Hemanjali Kadali, Sathwik Varma Mudduluri, Pawan Sai Krishna Reddy Kerelly, " Plausibility of BBR as CUBIC’S Replacement and proposed improvement to BBR using GENET" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 6, pp.303-309, November-December-2023. Available at doi : https://doi.org/10.32628/CSEIT2390660