Path Selection via Mutual Coherence Optimization in Network Monitoring

Periodically monitoring the state of internal links is important for network diagnosis. One of the major problems in tomography-based network monitoring is to select which paths to measure. In this paper, we propose a new path selection scheme by means of optimizing the mutual coherence of the routing matrix. The proposed scheme exploits the sparse characteristic of link status and follows the matrix design methods in sparse signal theory. By picking the paths with the minimum average mutual coherence, we can recover a sparse vector more accurately. The effectiveness of the proposed algorithms is analyzed theoretically. We conduct simulation experiments of delay estimation on both synthetic and real topologies. The results demonstrate that our scheme can select the most useful paths for network tomography with lowest cost in an acceptable time.