Traffic-Distribution Independent Network Cost Bounds and Cost Comparisons

Our recent work [1][2][3][4], has presented a reformulation of network design (routing), replacing the traffic matrix, by a set of linear constraints, representing a convex polyhedral traffic ensemble. Where applicable, this linear programming based method yields richer answers than using a traffic matrix. Firstly, network costs can be globally bounded over large scale distribution variations of traffic. This eliminates one of the weakenesses of classical network design (at least for planning purposes), in that the answers are dependent on error-prone traffic forecasts, which are error-prone. Secondly, inverse problems can be solved, with max/min bounds on network capacity and other metrics being derived from a prespecified cost. Thirdly, analyses of traffic matrices can be performed, detecting biases in selections. This paper presents results from these techniques to illustrate (a) Global cost bounds across large scale traffic vairations for realistic service provider networks. Our answers are across global traffic distribution variations keeping major traffic characteristics like total traffic, average router-distance and added-drop constraint.