Detecting shared congestion paths based on PCA

Most existing techniques detecting shared congestion paths are based on pair-wise comparison of paths with a common source or destination point. It is difficult to extend them to cluster paths with different sources and destinations. In this paper, we propose a scalable approach to cluster shared congestion paths based on PCA. This algorithm maps the delay measurement data of each path into a point in a new, low-dimensional space based on the factor loading matrix in PCA, which reflect correlation between paths. In this new space, points are close to each other if the corresponding paths share congestion. Then, the clustering analysis is applied to these points so as to identify shared congestion paths accurately. This algorithm is evaluated by NS2 simulations. The results show us that this algorithm has high accuracy.