UBC Theses and Dissertations
Accurate and efficient network monitoring on mesh topologies via network coding Gui, Jiaqi
Accurate and efficient measurement of network-internal characteristics is critical for management and maintenance of large-scale networks. In this thesis, we propose a linear algebraic network tomography (LANT) framework for active inference of link loss rates on mesh topologies via network coding. Probe packets are transmitted from the sources to the destinations along a set of paths. Intermediate nodes linearly combine the received probes and transmit the coded probes using pre-determined coding coefficients. Although a smaller probe size can reduce the bandwidth usage of the network, the inference framework is not valid if the probe size falls below a certain threshold. To this end, we establish a tight lower bound on probe size which is necessary for establishing the mappings between the contents of the received probes and the losses on the different sets of paths. Then, we develop algorithms to find the coding coefficients such that the lower bound on probe size is achieved. Furthermore, we propose a linear algebraic approach to developing consistent estimators of link loss rates, which converge to the actual loss rates as the number of probes increases. We show that using the LANT framework, the identifiability of a link, which only depends on the network topology, is a necessary and sufficient condition for the consistent estimation of its loss rate. Simulation results show that the LANT framework achieves better estimation accuracy than the belief propagation (BP) algorithm for large number of probe packets.
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