PROJECT TITLE :
Taming Both Predictable and Unpredictable Link Failures for NetworkTomography - 2018
Calculating fine-grained link metrics by using aggregated path measurements, referred to as network tomography, is an effective and economical method to facilitate numerous network operations, like network monitoring, load balancing, and fault diagnosis. Recently, there is a growing interest in the monitor placement downside that ensures link identifiability in a very network with link failures. Unfortunately, existing work either assumes an ideal failure prediction model where all failures will be predicted perfectly or makes pessimistic assumptions that every one failures are unpredictable. During this Project, we tend to study the matter of putting a minimum variety of monitors to identify additive link metrics [or additive by using the log(·) function, e.g., loss rates] from end-to-finish measurements among monitors with considering both predictable and unpredictable link failures. We have a tendency to propose a group of sturdy monitor placement algorithms with completely different performance-complexity tradeoffs to unravel this tomography downside. In particular, we have a tendency to show that the optimal (i.e., minimum) monitor placement is the solution to a hitting set downside, for that, we have a tendency to give a polynomial-time algorithm to construct the input. We tend to formally prove that the proposed algorithms can guarantee network identifiability against failures based on the graph theory. Trace-driven analysis results show the effectiveness and the robustness of our algorithms.
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