# Performance Impact of Nested Congestion Control on Transport-Layer Multipath Tunneling
## 5 Results
### Cubic over BBR and Inadequate Bottleneck Buffer Size  
In this section, we analyze how the degree of the achieved aggregation depends on the bottleneck buffer size relative to the number of packets that BBR attempts to keep within that buffer. It is worth noting that this number is typically around one bandwidth-delay product.  
Figure 8a shows four examples of throughput over time. As with the plots given in Figure 4, in Figure 8a, we observe the level of aggregation for four different testing  
Figure 6: Norm. vs. not using a tunnel.  
Figure 7: Norm. vs. using BBR tunnel.  
scenarios. This time CUBIC end-to-end congestion control is applied over different tunnels and with a different location of the server related to the multipath proxy. We can see again that only in the top case is full aggregation with a 100 Mbps rate achieved, with the other three scenarios offering much lower rates of aggregation. The reasons for this are explained in detail below, using the results from Figure 8b.  
Figure 8b,c show contour plots of a large set of results; the tunnel server-side endpoint location is specified as the tunnel RTT, as a percent of the end-to-end RTT; a high percent means the proxy is near the server. For each plot, the path asymmetry was set to 20 ms. The circle segment lines indicate a constant tunnel RTT corresponding to the maximum queuing delay, for the following bottleneck buffer sizes: a 25 ms tunnel RTT when the buffer was 250 packets large; 50 ms when the buffer was 500 packets large; no line when the buffer size was 1000 packets.  
**A long tunnel RTT reduces aggregation**. In Figure 8b, a circular segment line serves as a reference, representing all points where the tunnel RTT is constant at 25 ms. Clearly, a steep slope extends from the origin to the top-right corner, suggesting a significant influence of the tunnel RTT on aggregation. This is consistently observed across all nine plots in Figure 8c, albeit not as steep. As we move along this gradient towards the top-right corner, BBR/CCID5 becomes more likely to overflow the bottleneck buffer. Figure 8a illustrates this issue using time-series plots--the longer RTT results is less aggregation.  
**A small bottleneck buffer reduces aggregation**. Examining the middle row in Figure 8c, we observe the red circle segment shifting towards the upper-right corner as the bottleneck buffer size increases. A larger bottleneck buffer reduces the risk of the buffer overflowing when tunneling Cubic over BBR. Given the sharp shift from blue to red in the middle row, it is possible to notice a hard limit to how small the buffer may become before it has a severe impact on the aggregation. This pattern reproduces itself in the other two rows, highlighting the necessity for the bottleneck buffer to be large relative to the tunnel RTT.  
**BBRv2 tunnels enable more aggregation than BBR tunnels**. Examining the top row of Figure 8c, we observe that BBRv2 yields more aggregation compared to BBR or CCID5.  
Figure 8: Tunneling Cubic over BBR or CCID5. **(a)** Tunneling Cubic over BBR/CCID5. Blue and red are for paths 1 and 2, respectively; **(b)** Path asymmetry: 10 ms, buffer size: 250; **(c)** Variable bottleneck buffer sizes and congestion control algorithms.  
This is not surprising as BBRv2 reacts to loss to some degree, whereas the other two CCIDs do not. Therefore, we may expect that BBRv2 will avoid buffer overflows to a greater extent than BBR or CCID5. Poor aggregation is in this case caused by BBR or CCID5 trying to maintain more in-flight packets than the bottleneck buffer can hold.  
A receiver wait limit that allows for re-transmissions is beneficial. When using BBR as tunneling protocol across a set of scenarios, we found that (i) an added margin of 150 ms decreased the download completion time (DLCT) to 68.7% of the single-path DLCT, and 76.2% of the multipath DLCT with the standard margin of 5 ms; when (ii) using BBRv2 as tunneling protocol, the corresponding numbers were 65.4% and 74.0%, respectively.