Sparsifying network topologies for application guidance

Topology managers expose network information to applications to improve application-level resource management. An example for various ongoing standardization activities is Application-Layer Traffic Optimization (ALTO). Due to privacy and security constraints, exposed network information has to be filtered according to policies. As part of such policies, distance information can be abstracted by presenting coarser distances over pairs of clustered nodes rather than the precise distances between all node pairs. We refer to this process as distance sparsification. The contribution of this paper is two-fold, the first being a new policy system to enforce abstraction. The second and the main contribution addresses the algorithmic challenge. For the latter, we consider two types of distance sparsification algorithms. The first variant takes as input a matrix of pairwise distances. The sparsification algorithm produces a smaller distance matrix by clustering the nodes into clusters. The second variant instead collapses an edge-weighted graph. We measure the performance of the algorithms by the accuracy of the resulting sparsified distances, and we show that matrix sparsification outperforms graph sparsification. We further observe the trade-off between the accuracy and the size of the sparsified representation. In addition, we also extend our algorithms to handle labeled data, i. e., abstraction policies explicitly mark a number of destinations as reference points. Such additional information can improve the distance sparsification.