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Range tomography: combining the practicality of boolean tomography with the resolution of analog tomography

The objective of early network tomography approaches was to produce a point estimate for the performance of each network link (Analog tomography). When it became clear that the previous approach is error-prone in practice, research shifted to Boolean tomography where each link is estimated as either "good" or "bad". The Boolean approach is more practical but its resolution is too coarse. We propose a new tomography framework that combines the best of both worlds: we still distinguish between good and bad links (for practicality reasons) but we also infer a range estimate for the performance of each bad link. We apply the Range tomography framework in two path performance metric functions (Min and Sum) and propose an efficient algorithm for each problem. Together with simulations, we have also applied Range tomography in three operational networks allowing us to identify the location of bad links and to estimate their performance during congestion episodes. We also compare the proposed method with existing Analog and Boolean tomography algorithms.