Available Bandwidth Estimation
A Hidden Markov Model Approach
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The estimation of the available bandwidth in a network end-to-end path can be used to improve the performance of several network applications: network management, traffic engineering, flow and congestion control, routing protocols, admission control, among others. Current estimation tools use either the probe gap model or the probe rate model sampling techniques. In this work, bandwidth estimation tools and techniques are studied and a hidden Markov model (HMM) approach is investigated to incorporate the dynamics of the available bandwidth. The model is used by a proposed estimator that sample...
The estimation of the available bandwidth in a network end-to-end path can be used to improve the performance of several network applications: network management, traffic engineering, flow and congestion control, routing protocols, admission control, among others. Current estimation tools use either the probe gap model or the probe rate model sampling techniques. In this work, bandwidth estimation tools and techniques are studied and a hidden Markov model (HMM) approach is investigated to incorporate the dynamics of the available bandwidth. The model is used by a proposed estimator that samples the network and generates estimations adjusted by the HMM. This adjustment makes it possible to obtain acceptable estimation accuracy with a small number of samples and in a short period of time. The estimation called Traceband is fast, accurate and introduces low probing traffic to the network. A moving average technique can be implemented to smooth out the estimations and improves Traceband's accuracy even further.
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