A clustering approach for identifying and quantifying irregularities in interconnection networks

Support for arbitrary topologies has become more popular for system-area networks but very little has been done in trying to characterize their behavior and performance. Traditional parameters like diameter and bisection width are not sufficient for characterizing the irregularities that abound in such networks and fail to give much insight into throughput performance. A clustering approach for partitioning a network into clusters of richly-connected regions is proposed as a means of defining two performance-correlated characterization metrics: intercluster bandwidth index and intercluster link-cost index. The two characterization metrics are shown to have a strong correlation to saturation throughput when link and load distribution of a network is imbalanced. Simulation results also show that the clustering algorithm can be applied to a variety of network configurations and traffic scenarios, particularly irregular ones. With the proposed characterization metrics that correlate more strongly with performance, it is possible to classify networks into categories having similar performance.