# On Cross-Layer Interactions for Congestion Control in the Internet
## 5 Performance Evaluation
### Data Center Scenario, Common TCP  
The first simulation set is supposed to verify the overall performance and the cross-layer interactions between AQMs and TCP congestion control mechanisms in the Data Center scenario, i.e., with high-speed links and very short propagation delays, under the varying traffic load. The simulation results are gathered in the following tables. Table 2 presents the quartiles of single flows' throughput. Table 3 gives the [PERSON]'s index (inter-fairness) among all flows. Table 4 compares the average queue size with standard deviation and finally, Table 5 presents the packet drop ratio at the bottleneck link. The overall throughput is very similar for all TCPs and AQMs interactions; it varies from 0.92 to 0.93 Gbps in the uncongested network to 0.95-0.96 Gbps during heavy congestion. Neither the AQM algorithm nor the TCP version influences the bandwidth utilization. It also implies that the average throughput of every TCP connection is similar and equal to 103, 10.5, and 1.05 Mbps in uncongested, mildly, and heavily congested networks, respectively. However, when comparing the median values as well as the first and the third quartile of the flow's throughput (see Table 2), the differences are significant. First of all, regardless of the traffic load, the worst results, in terms of fairness, are observed for TCP Cubic and DT combinations. The median throughput is far below the average and, the flows' throughput distribution does not appear uniform. More precisely, during heavy congestion, DT with all TCPs is not able to provide acceptable fairness. It is not surprising, given the fact that only around 10 to 15% of all streams are able to transmit data packets effectively. Most connections are not even established due to continuous losses. Replacing DT with PIE or CoDel notably improves the fairness and similarity of the mean throughput registered by every flow (Table 2). It is also visible when comparing the Jain's index (Table 3); in the majority of all interactions with PIE or CoDel, it is equal or almost equal to 1.00. However, it is worth mentioning that in the uncongested network, C-TCP with DT provides better results in terms of fairness and throughput variations than in combination with any AQM. Nonetheless, the higher the network load, the poorer inter-fairness yielded by C-TCP and DT. In Table 4, the average queue size and standard deviation of the queue size are listed. The queue length variation over simulation time is depicted in Figure 2 (uncongested network), Figure 3 (during mild congestion), and Figure 4 (during heavy congestion). The highest values of the average queue size are noticed for all DT cases since DT fills the entire buffer before dropping packets. Moreover, in the uncongested network, DT causes major queue size variations, which implies the high variability of experienced delay. PIE and CoDel significantly reduce the average queue size and its variability. In the uncongested network, the best results in terms of queue length and stability are observed for CoDel and C-TCP. In the mildly congested network, both AQMs--PIE and CoDel--with any TCP combination, provide rather stable and much shorter than DT queues. There are no significant differences for any TCP-AQM pair. PIE in all TCP interactions yields shorter queues; actually, the average queue size of PIE is similar in all load scenarios. The average queue size provided by CoDel heavily depends on the congestion. Once again, in this load scenario, the most stable queue provides C-TCP and CoDel combination.