# Novel Multi-Level Dynamic Traffic Load-Balancing Protocol for Data Center
## 5 Results and Discussion
It is observed that MDTLB achieves less FCT than ECMP, because ECMP suffers more from hash collisions at a higher load. Moreover, MDTLB performs better at an 80% to 90% load compared to Clove and Herms for all types of flows, because MDTLB has better visibility and can react to the congestion. Furthermore, for all ranges expect 0.5-0.6, MDTLB had less FCT than most of the other protocols. MDTLB has lower a FCT than LetFlow, Drill, Presto, and CONGA for the range 0.3-0.5, because we observe that MDTLB reacts to the congestion in a more timely manner. Also, it is observed that MDTLB has achieved less than all of the other protocols in the range 0.7 until 0.9. Another observation is that the performance of MDTLB was more superior in long flows than other protocols, as we see in Figure 10b in comparison with Figure 10a,c. Here, we focus on the FCT of short flows, long flows, and both short and long flows with the web search and data-mining workloads, which are both widely used workloads from deployed data centers, as shown in Figures 11 and 12, respectively. **Under the web-search workload**  
We observe that MDTLB has an average performance compared with other protocols. The reason is that the web-search considers bursty small flows. The frequent arrival of flows then creates flow gaps by breaking large flows into small flows. As shown in Figure 11, the FCT of MDTLB achieves one with the increasing load as compared to other protocols. However, excessive rerouting opportunities may negatively affect small flows without caution rerouting. As we can see in Figure 11a,c, the FCT for small flows grow dramatically as the load increases, which is because small flows are broken into several flowlets under heavy load; thus, they are heavily affected by packet reordering and congestion mismatch. In comparison, we observed that the FCT of MDTLB was lower compared with ECMP, Hermes, Clove, LetFlow, and CONGA due to being interpreted by the multi-level path characterization of MDTLB and the adaptive settings of the threshold. **Under the data-mining workload**  
In Figure 12, we observe that the MDTLB performance is better then Clove, Hermes, and Drill, because the data-mining workload is less bursty, and so the visibility of the network congestion becomes especially important to balance the load effectively. Also, we observe that MDTLB has achieved a higher FCT than DRB. Figure 12b shows that MDTLB outperforms ECMP, CONGA, Clove, Letflow, Presto, DRB, Hermes, and Drill; this is because MDTLB can effectively resolve the collision of large flows due to the multi-level setting aspects in it. However, MDTLB provides an FCT lower than most protocols, including DRB, when the workload increases. Furthermore, our protocol has achieved a lower FCT than Hermes, LetFlow, and Clove, because these solutions without good visibility can hardly balance the traffic. The average throughput was evaluated for the different flows. Then, a comparison study has been made between the proposed protocol and the existing protocols (shown in Figures 13-15). It is observed that MDTLB has achieved the best throughput compared with existing protocols for data-mining flows. It is also evident that the proposed protocol is the top among three existing protocols for general flows with CONGA and Presto, while it was among the top five protocols for web flow. This is consistent with the nature of web-search flows that have small bursts flows.