Effective Flow Aggregation For Traffic Engineering

Although the use of software-defined networking (SDN) enables routes of packets to be controlled with finer granularity (down to the individual flow level) by using traffic engineering (TE) and thereby enables better balancing of the link loads, the corresponding increase in the number of states that need to be managed at routers and controller is problematic in large-scale networks. Aggregating flows into macro flows and assigning routes by macro flow should be an effective approach to solving this problem. However, when macro flows are constructed as TE targets, variations of traffic rates in each macro flow should be minimized to improve route stability. We propose two methods for generating macro flows: one is based on a greedy algorithm that minimizes the variation in rates, and the other clusters micro flows with similar traffic variation patterns into groups and optimizes the traffic ratio of extracted from each cluster to aggregate into each macro flow. Evaluation using traffic demand matrixes for 48 hours of Internet2 traffic demonstrated that the proposed methods can reduce the number of TE targets to about 1/50 similar to 1/400 without degrading the link-load balancing effect of TE.