RavenFlow: Congestion-Aware Load Balancing in 5G Base Station Network

摘要

The fifth generation mobile network(5G) is coming, and the base station network carries more and more pressure due to the higher rate demand of users. Load balancing traffic is crucial for high link utilization and low latency. Most of the research on load balancing algorithms focuses on data center scenarios and works based on TCP. Traditional algorithm used in 5G base station network is Equal Cost Multipath Routing(ECMP), which has poor performance due to its congestion agnostic nature. Other existing cogestion-aware algorithms work well in data center networks. However, the transport protocol and the traffic type is different in 5G base station network. It will change the performance of the original algorithms and cause the challenges to implement the schemes. In this paper, we propose RavenFlow, an effective load balancing algorithm working on layer 2 while using UDP. It makes use of different congestion information and eliminates the dependence on reverse packets. We evaluate the performance of RavenFlow in ns-3 simulator under typical 5G traffic. The result shows that RavenFlow achieves shorter Flow Completion Time(FCT) and smaller difference of utilization for different links.