An analysis of BGP convergence properties
Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication
Stable internet routing without global coordination
IEEE/ACM Transactions on Networking (TON)
The stable paths problem and interdomain routing
IEEE/ACM Transactions on Networking (TON)
Implications of autonomy for the expressiveness of policy routing
Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications
A BGP-based mechanism for lowest-cost routing
Distributed Computing - Special issue: PODC 02
Mechanism design for policy routing
Distributed Computing - Special issue: PODC 04
Subjective-Cost policy routing
WINE'05 Proceedings of the First international conference on Internet and Network Economics
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
Rationality and traffic attraction: incentives for honest path announcements in bgp
Proceedings of the ACM SIGCOMM 2008 conference on Data communication
SIAM Journal on Computing
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We study a model of path-vector routing in which nodes' routing policies are based on subjective cost assessments of alternative routes. The routes are constrained by the requirement that all routes to a given destination must be confluent. We show that it is NP-hard to determine whether there is a set of stable routes. We also show that it is NP-hard to find a set of confluent routes that minimizes the total subjective cost; it is hard even to approximate the minimum cost closely. These hardness results hold even for very restricted classes of subjective costs. We then consider a model in which the subjective costs are based on the relative importance nodes place on a small number of objective cost measures. We show that a small number of confluent routing trees is sufficient for each node to have a route that nearly minimizes its subjective cost. We show that this scheme is trivially strategy proof and that it can be computed easily with a distributed algorithm. Furthermore, we prove a lower bound on the number of trees required to contain a (1+@e)-approximately optimal route for each node and show that our scheme is nearly optimal in this respect.