Revealing the optimality gap for Traffic Engineering algorithms

Traffic Engineering (TE) is important and necessary in order to fully utilize the existing network resources and reduce capital expenditure. Given its importance, many algorithms have been proposed in the literature. Unfortunately, there is still not a consistent methodology to evaluate these algorithms. Worse yet, some variants of the traffic engineering problem are known to be NP-hard. Thus, given a particular TE problem, in general, it is not possible to know the optimal solution; hence, it is difficult to assess how a particular heuristic algorithm performs. Even though several heuristic algorithms could be used to validate each other, the best solution from these algorithms could still be very far away from the optimal solution. We propose a novel methodology to evaluate TE algorithms. In this methodology, we construct TE problems with known optimal solutions and we then use these TE problem instances to test the performance of TE algorithms. We found that some TE algorithms perform poorly, and the result deviates from the optimum further as the problem size gets bigger. Our results suggest that there is large room for algorithm improvements and further research is required. Even though we only demonstrate the power of the methodology in the context of traffic engineering algorithms, the methodology is general enough that it could be applied in many other areas as well.