# Performance Impact of Nested Congestion Control on Transport-Layer Multipath Tunneling
## 5 Results
### Performance Comparison of Nested CCA Combinations  
This section presents the achieved aggregation results for all CCA combinations and scenarios described in Section 4.4. Each scenario was repeated 10 times for every combination of server and tunnel CCA. Results are presented as the download completion time (DLCT) when downloading a 300 MB file, relative to the DLCT without a tunnel. Recall that the optimal DLCT is around 50% when using the multipath framework as this would allow to double the available capacity. A relative DLCT of more than 100% indicates that the DLCT increases when using multipath compared to only single path, which clearly should be avoided. Figure 10 shows the average DLCT over a multipath tunnel relative to the average DLCT without a tunnel for a set of 15 scenarios, repeated 10 times for each CCA combination. In the figure, \(SHORT\) and \(LONG\) refer to the RTT; \(BUF\) denotes the bottleneck MAC layer buffer size, in packets, in both paths; the prefix \(ASYM\) refers to the difference in path RTT; \(NEAR\) and \(DIST\) refer to the proxy being near to, or far from the user; \(CDL0\) and \(CDL1\) refers to whether CoDel was used at the bottleneck or not. The _bright_ and _dark_ highlights help to indicate the RTT; each result is paired with the result of the opposite scenario for easy comparison. The results in Figure 9(a),b are a subset of those in Figure 9(c),f.  
Figure 9(a),b arrange these results by scenario for a subset of all scenarios, while Figure 9(c),f arrange the same results by CCA combination. Here, we can see how some configurations particularly impact some CCA combinations. _BBR over BBR/CCID5_: The four top bars in Figure 9(c) demonstrate that when tunneling BBR over BBR the performance improves when the proxy is deployed near the user, as explained in Section 4.1. Figure 9(c) shows that a small bottleneck buffer or presence of CoDel also inhibits aggregation, likely due to BBR's limited reaction to loss. A similar pattern is observed when tunneling BBR over CCID5, albeit with a more favorable outcome for CCID5. Our conclusion is that CCID5 performs better, in part because it bases its congestion window (CWND) on packets rather than bytes. While it is plausible that the ability of DCCP and CCID5 to avoid single-path head-of-line blocking contributed to this performance disparity, this explanation remains speculative and requires further validation. _Cubic over BBR/CCID5_: The six top bars in Figure 9(d) demonstrate that a short tunnel and sufficiently large bottleneck buffer are beneficial when tunneling Cubic over BBR/CCID5. As explained in Section 4.2, these scenarios experience fewer packet losses. CoDel is another source of packet losses, additionally deteriorating the performance. A similar pattern is seen when tunneling BBR over CCID5, but the performance is worse for CCID5. The visibly longer minimum-RTT probing periods of CCID5 have an adverse impact on the more sensitive Cubic congestion control algorithm. In addition, Section 4.2 already illustrated that both using BBRv2 instead of BBR and tuning the tunnel receiver to wait longer for re-transmitted packets could improve the performance significantly. BBR over NewReno/CCID2:In Figure 10e, the top eight bars demonstrate that when tunneling BBR over NewReno/CCID2, a long RTT or a small bottleneck buffer results in very good performance. This was previously analyzed in Section 4.3. With congestion being one of the fundamental challenges, CoDel at the bottleneck may also improve aggregation, although not when the RTT is long. While a long end-to-end RTT improves the performance, this is not something that is in itself desirable or feasible. Our conclusion, given these results, is that NewReno and CCID2 are bad candidates for a tunneling CCA. Cubic over NewReno/CCID2:This combination was not dealt with in previous sections. Part of the reason we omitted a deeper evaluation of this combination was that we were unable to pose a good single hypothesis as to which factors determined the performance.