Path Trading: Fast Algorithms, Smoothed Analysis, and Hardness Results
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- Berger A., Röglin H., van der Zwaan R. (2011) Path Trading: Fast Algorithms, Smoothed Analysis, and Hardness Results. In: Pardalos P.M., Rebennack S. (eds) Experimental Algorithms. SEA 2011. Lecture Notes in Computer Science, vol 6630. Springer, Berlin, Heidelberg
The Border Gateway Protocol (BGP) serves as the main routing protocol of the Internet and ensures network reachability among autonomous systems (ASes). When traffic is forwarded between the many ASes on the Internet according to that protocol, each AS selfishly routes the traffic inside its own network according to some internal protocol that supports the local objectives of the AS. We consider possibilities of achieving higher global performance in such systems while maintaining the objectives and costs of the individual ASes. In particular, we consider how path trading, i.e. deviations from routing the traffic using individually optimal protocols, can lead to a better global performance. Shavitt and Singer (“Limitations and Possibilities of Path Trading between Autonomous Systems”, INFOCOM 2010) were the first to consider the computational complexity of finding such path trading solutions. They show that the problem is weakly NP-hard and provide a dynamic program to find path trades between pairs of ASes.
In this paper we improve upon their results, both theoretically and practically. First, we show that finding path trades between sets of ASes is also strongly NP-hard. Moreover, we provide an algorithm that finds all Pareto-optimal path trades for a pair of two ASes. While in principal the number of Pareto-optimal path trades can be exponential, in our experiments this number was typically small. We use the framework of smoothed analysis to give theoretical evidence that this is a general phenomenon, and not only limited to the instances on which we performed experiments. The computational results show that our algorithm yields far superior running times and can solve considerably larger instances than the previous dynamic program.
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