# Dynamic Traffic Scheduling and Congestion Control across Data Centers Based on SDN
## 3 The Design and Implementation of DSCSD
### Congestion Control with Multilevel Feedback Queue
With this mechanism, we can provide a new solution to control the congestion caused by the prolonged occupation of malicious flows. As shown in Figure 8, we demonstrated the comparison of the link delay in the two aforementioned cases. We can easily draw the conclusion that by using the congestion control queue, the congestion caused by malicious flows can be well addressed with a relative reduction in link delay. Figure 6: Real-time bandwidth without congestion control queues. Figure 7: Real-time bandwidth with congestion control queues. ## 5 Conclusions and Future Work

In this paper, we focused on the problem of traffic scheduling and congestion control across data centers and aimed to provide an approach that could greatly improve link utilization. To realize this goal, we designed DSCSD, a dynamic traffic scheduling and congestion control scheme across data centers based on SDN. The moment a flow arrives, it relies on the connection of traffic parameters and link information to select paths. Furthermore, it can achieve real-time dynamic scheduling to avoid congestion caused by the burst of instantaneous traffic, and can also balance the link loads. Compared with traditional approaches, the experiment and analysis had an obvious effect on the classification and diversion of flows, thereby improving the link utilization across data centers. Better than the SDN-based scheme with threshold value, the real-time monitoring and dynamic scheduling of the shortest paths were fully reflected. Meanwhile, we innovatively adopted the mechanism of a multilevel feedback queue for congestion control, which is suitable for different types of flows and can implement anomaly detection by preventing malicious flow from chronically occupying the bandwidth. Our proposed DSCSD scheme can be easily deployed to the existing data center networks to deal with the low utilization of link resources such as the data centers of live video streaming platforms and online video platforms. Although our scheme solved the traffic scheduling problem efficiently, there are still some limitations. First, our scheme is based on SDN, but DSCSD is not applicable in traditional network environments or a hybrid network environment. Second, more fine-grained and flexible hierarchical control might be helpful to further enhance the experimental result. In addition, we did not take into account the issue of energy saving in the traffic scheduling and congestion control of data center networks. Accordingly, we would like to enrich DSCSD with energy management in the future. [PERSON] conceived and designed the system model and algorithm; [PERSON] was responsible for literature retrieval and chart making; [PERSON] performed the experiments, analyzed the data, and wrote the paper; [PERSON] designed the research plan, conceived the algorithm model, and polished the paper. This research was funded by the [National Natural Science Foundation of China] grant number [61502008], the [Key Scientific Research Project of Henan Higher Education] grant number [16A520084], the [Natural Science Foundation of Anhui Province] grant number [1508085 QF132] and [the Doctoral Research Start-Up Funds Project of Anhui University]. The authors declare no conflict of interest. Figure 8: Comparison of the link delay. ## References

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