Abstract
Methods are developed to estimate the source–destination traffic distribution matrix of a packet network using only aggregate link and source/sink traffic measurements. The methods are useful for network planning and monitoring of large packet networks, where it is impractical to gather measurement data on every end-to-end traffic flow. The first method assumes that the distribution matrix is time-invariant. This method is of theoretical interest but provides the foundation for developing a method for the realistic case of a time-varying matrix. The second method assumes that the matrix is time-varying. It uses linear programming (LP) to find a distribution matrix that optimally fits the measurement data. A practical problem with the first two methods is that the computational requirements increase as the square of the number of network nodes. The third method is a fast exact decomposition procedure for the time-invariant case that scales with the network size. The maximum number of unknowns that needs to be solved simultaneously is equal to the number of network nodes. The final method is a fast decomposition procedure for the time-varying case. This procedure scales with the network size. It uses LP to find an approximate distribution matrix that optimally fits the measurement data. The methods are applied to simulated example networks to illustrate the accuracy and speed.
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Conway, A.E., Li, M. Estimating the Traffic Distribution Matrix of a Packet-Switched Network Using Aggregate Traffic Measurements. Telecommunication Systems 24, 9–28 (2003). https://doi.org/10.1023/A:1025891325140
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DOI: https://doi.org/10.1023/A:1025891325140