Clustering Algorithms for the Optimization of Communication Graphs

One of the main goals in optimizing communication networks is to enhance performances by minimizing the number of message hops, i.e. the number of graph nodes traversed by a message. Most of the optimization techniques are based on clustering, i.e., the network layout is reconfigured in sub-networks. Network clustering has been largely studied in the literature but most of the available algorithms are application dependent. In this paper we restrict our attention to algorithms based on the location of the median points, in order to build clusters with a balanced number of elements and to minimize communication time. We present two algorithms and relative experimental results about the quality of the computed clusterizations, in terms of the minimum number of computed hops. One algorithm is based on the well-known multi-median heuristic algorithm, while the other adopts a greedy approach, i.e., at each step the algorithm computes clusters farther and farther from each central node. To the achieved clusterization we apply a further step, which consists in finding a virtual path layout according to Gerstel’s (VPPL) algorithm. The adopted criterium for our experimental comparisons is the optimality, in terms of the number of signal hops, of the achieved virtual path layout. The experiments are carried out upon a set of networks representing real environments.