Redirigiendo al acceso original de articulo en 24 segundos...
Inicio  /  Future Internet  /  Vol: 10 Núm: 7 Par: July (2018)  /  Artículo
ARTÍCULO
TITULO

Dynamic Traffic Scheduling and Congestion Control across Data Centers Based on SDN

Dong Sun    
Kaixin Zhao    
Yaming Fang and Jie Cui    

Resumen

Software-defined Networking (SDN) and Data Center Network (DCN) are receiving considerable attention and eliciting widespread interest from both academia and industry. When the traditionally shortest path routing protocol among multiple data centers is used, congestion will frequently occur in the shortest path link, which may severely reduce the quality of network services due to long delay and low throughput. The flexibility and agility of SDN can effectively ameliorate the aforementioned problem. However, the utilization of link resources across data centers is still insufficient, and has not yet been well addressed. In this paper, we focused on this issue and proposed an intelligent approach of real-time processing and dynamic scheduling that could make full use of the network resources. The traffic among the data centers could be classified into different types, and different strategies were proposed for these types of real-time traffic. Considering the prolonged occupation of the bandwidth by malicious flows, we employed the multilevel feedback queue mechanism and proposed an effective congestion control algorithm. Simulation experiments showed that our scheme exhibited the favorable feasibility and demonstrated a better traffic scheduling effect and great improvement in bandwidth utilization across data centers.

 Artículos similares

       
 
Mengchi Xing, Haojiang Deng and Rui Han    
The 5G core network adopts a Control and User Plane Separation (CUPS) architecture to meet the challenges of low-latency business requirements. In this architecture, a balance between management costs and User Experience (UE) is achieved by moving User P... ver más
Revista: Future Internet

 
Zhi Cai, Fangzhe Liu, Qiong Qi, Xing Su, Limin Guo and Zhiming Ding    
Urban rail transit is an essential part of the urban public transportation system. The reasonable spatial data visualization of urban rail transit stations can provide a more intuitive way for the majority of travelers to arrange travel plans and find de... ver más

 
Zhiqiang Han, Gang Xie, Yongjun Zhou, Yajuan Zhuo, Yelu Wang and Lin Shen    
To overcome the limitations of using time interval division to calculate the bridge impact coefficient (IM), a sieving method has been proposed. This method employs multiple sieves on bridge time?history curve samples to ultimately obtain the bridge impa... ver más
Revista: Buildings

 
Stefania Zourlidou, Monika Sester and Shaohan Hu    
In this paper, a new method is proposed to detect traffic regulations at intersections using GPS traces. The knowledge of traffic rules for regulated locations can help various location-based applications in the context of Smart Cities, such as the accur... ver más

 
Zhenxin Li, Yong Han, Zhenyu Xu, Zhihao Zhang, Zhixian Sun and Ge Chen    
Traffic forecasting has always been an important part of intelligent transportation systems. At present, spatiotemporal graph neural networks are widely used to capture spatiotemporal dependencies. However, most spatiotemporal graph neural networks use a... ver más