• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.4
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• pp.151-161
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• 2005

For Internet service providers (ISPs), there are three common types of interconnection agreements : private peering, public peering and transit. One of the most important problems for a single ISP is to determine which other ISPs to interconnect with, and under which agreements. The problem can be then to find a set of private peering providers, transit providers and Internet exchanges (IXs) when the following input data are assumed to be given : a set of BGP addresses with traffic demands, and a set of potential service providers (Private peering/transit providers and IXs) with routing information, cost functions and capacities. The objective is to minimize the total interconnection cost. We show that the problem is NP-hard, give a mixed-integer programming model, and propose a heuristic algorithm. Computational experience with a set of test instances shows the remarkable performance of the proposed algorithm of rapidly generating near-optimal solutions.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.10a
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• pp.388-393
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• 2005

Private peering, public peering and transit are three common types of interconnection agreements between providers in the Internet. An important decision that an Internet service provider (ISP) has to make is which private peering/transit ISPs and Internet exchanges (IXs) to connect with to transfer traffic at a minimal cost. In this paper, we deal with the problem to find the minimum cost set of private peering/transit ISPs and IXs for a single ISP. There are given a set of destinations with traffic demands, and a set of potential private peering/transit ISPs and IXs with routing information (routes per destination, the average AS-hop count to each destination, etc.), cost functions and capacities. Our study first considers all the three interconnection types commonly used in real world practices. We show that the problem is NP-hard, and propose a heuristic algorithm for it. We then evaluate the quality of the heuristic solutions for a set of test instances via comparison with the optimal ones obtained by solving a mixed integer programming formulation of the problem. Computational results show that the proposed algorithm provides near-optimal solutions in a fast time.