# Performance Evaluation of TCP BBRv3 in Networks with Multiple Round Trip Times
## 6 Conclusions and Future Work  
This paper has presented the results of an extensive evaluation of all BBR iterations, thus analyzing network performance metrics, including throughput, loss ratio, and intra- and interprotocol fairness in scenarios with various distributions of RTTs. This study verified whether the introduced amendments in BBRv3 achieved their intended objectives, which namely include the following:  
* Enhanced convergence in shallow buffers following packet losses;
* Improved convergence in deep buffers in the absence of loss signals;
* Reduced queuing delay irrespective of buffer size. This study did not observe the anticipated enhanced convergence in the recent version. While BBRv3 did enhance fairness compared to BBRv2, in many cases, BBRv1 yielded results that were at least as satisfactory, and in numerous instances, it exhibited a 10-20% higher fairness index. The sole advantage of this version over the original is a more stable connection and a negligible loss ratio. BBRv3 generally improved in maintaining stable and low queues. However, further investigation is required regarding buffer occupancy when the size equals or exceeds 10*BDP, particularly concerning performance in scenarios involving numerous flows across paths with significantly divergent RTT values. Nevertheless, a buffer size of at least 2*BDP is recommended when deploying BBRv3. With 2*BDP settings, BBRv3 demonstrates nearly a 60% improvement in the average queue size compared to the first version and approximately a 50% improvement compared to the second. BBRv3 enhanced bandwidth availability for CUBIC streams, especially when the paths' RTTs were similar. In a multi-RTT network with shallow buffers, BBRv3 enhanced fairness by 12%. In deep buffer scenarios, the bandwidth sharing among combined CUBIC and BBR flows was improved by 5%. However, further optimization is necessary to ensure equitable bandwidth allocation between streams and to enhance the convergence speed. The fairness of BBRv3 depends solely on the buffer size. Moreover, additional optimization is needed to improve fairness in networks with varying RTTs. Unfortunately, this challenge is not unique to BBR; none of the currently employed CC algorithms yielded satisfactory results in this regard. BBRv2 and BBRv3 significantly enhanced bandwidth utilization compared to the initial version, particularly in scenarios with very short buffers. Depending on the RTT ratio, the improvement was as high as 80%. Moreover, in these scenarios, both versions effectively mitigated the issue of excessive loss ratio, thus reducing it by a factor of 10-20. A comprehensive evaluation of the TCP necessitates the analysis of results across a wide range of diverse scenarios. This paper specifically tackles the issue of fairness to other flows and the TCP CUBIC in networks characterized by varying RTTs. However, further research is necessary to explore the following areas: lossy networks, enhancing performance through ECN and/or AQM, assessing performance in networks with distinct conditions such as Wi-Fi and cellular networks, conducting additional scenarios involving CUBIC, varying the number of flows, and analyzing diverse traffic patterns. The author believes that the simulation environment provided with this paper will facilitate extensive research and subsequent enhancements in this domain. The work was carried out within the statutory research project of the Department of Computer Networks and Systems. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The original data presented in the study are openly available in GitLab at [[https://gitlab.com/agnieszka.brachman/ns-3-dev/-/blob/dev-bbrv3-fixed](https://gitlab.com/agnieszka.brachman/ns-3-dev/-/blob/dev-bbrv3-fixed)]([https://gitlab.com/agnieszka.brachman/ns-3-dev/-/blob/dev-bbrv3-fixed](https://gitlab.com/agnieszka.brachman/ns-3-dev/-/blob/dev-bbrv3-fixed)) (accessed on 6 May 2024).