## 6 Conclusions and Future Work  
In the research of this article, we address the traffic transmission problem in the cloud data center network through a proactive dynamic load-balanced scheduling method. Additionally, we test it in two highly universal topologies: partial mesh network topology and tree network topology FPN. Compared with the existing static load-balancing algorithm FCFS and dynamic load-balancing algorithm one-hop DLBS, our algorithm has the following three main advantages: first, we take into account the delay-sensitive characteristics of small flows and use dynamic routing networks to filter out large flows, so as to ensure the bandwidth allocated to small flows and reduce delay; second, we adopt proactive  
Figure 10: Three algorithms in partial mesh network model under 20 MB. Figure 9: Three algorithms in partial mesh network model under 40 MB. congestion control, calculating the bandwidth allocated to each flow in advance according to the network conditions; third, we can adjust the bandwidth allocation ratio of the two sub-nets to better adapt to different network conditions and transmission tasks. The simulation results prove that our MFLBS algorithm performs better than the representative static load-balanced scheduling algorithm FCFS and dynamic load-balanced scheduling algorithm one-hop DLBS. For example, in the FPN network model, the MFLBS algorithm can maintain high throughput and low latency in a wider range of tasks than the other two algorithms; and in the partial mesh model, compared to the one-hop DLBS, our MFLBS improves throughput by 58% and reduces average latency by 20%. These results show that our MFLBS algorithm can effectively proactively control congestion and significantly improve network throughput and reduce the delay by dynamically balancing the traffic. In the future, we plan to expand our existing work from the following two perspectives:  
* Network model. The existing algorithms perform better in networks with higher node connectivity. Therefore, we hope to improve the bandwidth allocation method of the flow and improve the performance of the node in the network model with low connectivity. * Identification of the stream. As the number of tasks increases and the types of mixed flows increase, we plan to further distinguish and identify different types of flows and perform network scheduling in consideration of transmission requirements in detail. Conceptualization, A.A.-D.; Methodology, B.S. ; Project administration, X.Z. ; Validation, Y.C. ; Writing--original draft, Y.C. ; Writing--review & editing, M.A.-D. All authors have read and agreed to the published version of the manuscript. The authors extend their appreciation to National Key Research and Development Program of China (International Technology Cooperation Project No.2021 YFE014400) and National Science Foundation of China (No.42175194) for funding this work. This work was supported in part by the Deanship of Scientific Research at King Saud University through research group no. RGP-264. The authors declare no conflict of interest. [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] TSAN: Synthesized View Quality Enhancement via Two-Stream Attention Network for 3D-HEVC. _IEEE Trans. Circuits Syst. Video Technol._**2022**, _32_, 345-358. [CrossRef]
* _[PERSON]; [PERSON] A Survey on Optical Interconnects for Data Centers. _IEEE Commun. Surv. Tutor._**2012**, _14_, 1021-1036. [CrossRef]
* _[PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Software-Defined Networking: A Comprehensive Survey. _Proc. IEEE_**2015**, _103_, 14-76. [CrossRef]
* _[PERSON]; [PERSON]; [PERSON] A Survey on Software-Defined Network and OpenFlow: From Concept to Implementation. _IEEE Commun. Surv. Tutor._**2014**, _16_, 2181-2206. [CrossRef]
* _[PERSON]; [PERSON]; [PERSON] Dynamic load balancing on Web-server systems. _IEEE Internet Comput._**1999**, \(3\), 28-39. [CrossRef]
* _[PERSON]; [PERSON]; [PERSON]; [PERSON] A Performance Study of Static Task Scheduling Heuristics on Cloud-Scale Acceleration Architecture.