Probabilistic Codebook-Based Fault Localization in Data Networks

This paper shows a codebook-based proposal for identifying simultaneous faults in data networks. It includes three main contributions. The first consists of the probabilitstic reduced search space heuristic (PRSSH), which aims to significantly reduce the cardinality of the candidate set of solutions through a so-called compatibility filtering. It is applied to the codebook optimized by the second contribution, which is a solution of the codebook optimization problem. It is built on the weighted set covering optimization problem and consists of a heuristic, named minimum Hamming distance increment maximization heuristic (MHDIM-HEU). Performance has been evaluated both by applying the proposed techniques to some sample networks, simulated by using the end-to-end connectivity service model, and by using an experimental codebook generated by using data collected from a real, nation-wide, next generation network. This codebook has been used to simulate fault effects, which were used to analyze the proposal. We compared the PRSSH approach with a previous proposal, named incremental hypothesis update (IHU), and the MHDIM-HEU with a random heuristic and an optimal branch and bound solution (only for very small network size). Results show the effectiveness of our proposals: PRSSH can decrease the false positive rate up to 66% with respect to IHU, with a significantly reduced processing time (two orders of magnitude). As for MHDIM-HEU, for the same target minimum Hamming distance it requires about half of the symptoms of the random heuristic, with performance very close to the optimal approach. As a third contribution, we evaluated the performance of the PRSSH approach over the codebook optimized by means of MHDIM-HEU, in order to evaluate the impact of the codebook compression on the fault localization performance. Numerical results confirm the goodness of the joint application of both approaches.