Method for optimizing network "Point of Presence" locations

Abstract

A methodology for optimizing the placement of network “Points of Presence” (POPs) across the carrier's entire network (i.e., a “global” solution”) utilizes carefully constructed customer clustering and simulated annealing methodology to create a cost-efficient solution. The customer base is first partitioned into a plurality of clusters such that the customers within each cluster are closer to its centroid than the centroid of any other cluster (e.g., applying the k-means clustering algorithm or any other suitable method of partitioning the customer base). A linear algorithm process is used to minimize the costs associated with the number of placement of POPs within each cluster. A simulated annealing (SA) process is then used to iterate the entire set of potential POP locations until a compact, steady-state solution is achieved (or, alternatively, until a given number of iterations has been performed). In a preferred embodiment, a number of iterations are performed at each “temperature” in the simulating annealing process to further improve the result (this iterative process referred to in the art as “intensification”).

Dependencies are inferred from the claim text.