# On Cross-Layer Interactions for Congestion Control in the Internet
## 5 Performance Evaluation
### Access Link, Common TCP
Nevertheless, the values of [PERSON]'s index are noticeably worse than in the Data Center scenario, which implies that the varied RTT hinders providing a fair share of the bottleneck bandwidth. The queue size and stability dependence on the TCP version and buffer management strategy are presented in Table 8. In the DC scenario, we could observe that the average queue size and its stability depend on the level of congestion and the buffer management strategy applied at the router. In this scenario, it is apparent that the TCP version also influences the queue behavior. In most cases, TCP Cubic leads to slightly higher average queue size in comparison to New Reno and C-TCP. In the uncongested network, Drop Tail strategy with New Reno and Cubic leads to considerable average queue size and queue size variations, which implies the high variability of experienced delay. Surprisingly C-TCP, even with DT, is able to maintain a relatively short average queue size, however with high fluctuations. C-TCP with PIE or CoDel provides the best results in terms of the average queue length. All TCP versions benefit from interacting with PIE or CoDel in terms of the average queue size and the standard deviation of the queue size; all TCP-AQM pairs provide a rather low and stable queue (see Figure 5). During mild and heavy congestion, DT leads to a persistently full queue. Although the observed fluctuations are not significant, a full queue implies long delays, which may be unacceptable in many applications. PIE, in combination with all TCPs, achieves remarkably better results than CoDel. During heavy congestion, CoDel, similarly as in the DC scenario, oscillates between the maximum and target values, however at a significantly lower frequency. The queue size variability in the mildly congested network is depicted in Figure 6 and during heavy congestion in Figure 7. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **DT** & **PIE** & **CoDel** \\ \hline \multirow{3}{*}{UC} & New Reno & 727 (142) & 34 (27) & 29 (23) \\ \cline{2-5}  & Cubic & 772 (85) & 48 (26) & 50 (18) \\ \cline{2-5}  & C-TCP & 167 (126) & 23 (20) & 25 (20) \\ \hline \multirow{3}{*}{MC} & New Reno & 832 (50) & 54 (26) & 94 (36) \\ \cline{2-5}  & Cubic & 899 (1) & 52 (22) & 91 (30) \\ \cline{2-5}  & C-TCP & 830 (46) & 42 (17) & 68 (18) \\ \hline \multirow{3}{*}{HC} & New Reno & 891 (10) & 44 (21) & 588 (272) \\ \cline{2-5}  & Cubic & 894 (7) & 43 (20) & 656 (280) \\ \cline{1-1} \cline{2-5}  & C-TCP & 893 (8) & 45 (21) & 592 (281) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Mean queue length and standard deviation of queue length [pkts] in the Access Link scenario with common TCPs, UC—uncongested, MC—mildly congested, HC—heavily congested network. TCP Cubic leads to the highest drop ratio compared to the other two TCPs (see Table 9). On the other hand, C-TCP, regardless of the queue management, provides the least packet losses. In the uncongested network, the TCP Cubic and DT combination achieves the worst results in terms of the drop ratio; replacing DT with any evaluated AQM considerably improves this metric. Nevertheless, the drop ratio is relatively small in this network load scenario. In the mildly congested network, C-TCP once again provides the lowest and Cubic the highest drop ratio. During heavy congestion, it is visible that the drop ratio depends on the applied AQM, i.e., PIE causes slightly more losses than CoDel and DT; however, it provides the shortest and most stable queue at the same time. Similarly, as in the previous scenario, improvements can be proposed for the buffer management strategy. CoDel experiences similar problems with oscillations under heavy congestion, however a slower network causes less frequent oscillations. The average RTT is close to the nominal RTT, therefore it is clear that updating only the interval parameter according to the network speed, is not enough. It is necessary to increase the number of rejected packets per every \(\upmu\). This number could depend on the increase rate of the average queue size.