Scalable Traffic Engineering for Higher Throughput in Heavily-loaded Software Defined Networks

Existing traffic engineering (TE) solutions perform well for software defined network (SDN) in average cases. However, during peak hours, bursty traffic spikes are challenging to handle, because it is difficult to react in time and guarantee high performance even after failures with limited flow entries.
We propose TED, a scalable TE system that can guarantee high throughput in peak hours. TED can quickly compute a group of maximum number of edge-disjoint paths for each ingress-egress switch pair. Such paths are suitable for well connected networks with unique edge capacity and TED is not limited to use only these paths. We design two methods to select paths under the limit of flow table size. We then input the selected paths to TED to minimize the maximum link utilization. In case of large traffic matrix making the maximum link utilization larger than 1, we input the utilization and the traffic matrix to the optimization of maximizing overall throughput under a new constrain. Thus we obtain a realistic traffic matrix, which has the maximum overall throughput and guarantees no traffic starvation. Experiments show that TED has much better performance for heavily-loaded SDN and has 10% higher probability to satisfy all (> 99.99%) the traffic after a single link failure for G-Scale topology than Smore under the same limit of flow table size.