A Hidden Markov Model approach to available bandwidth estimation and monitoring

Available bandwidth estimation techniques are being used in network monitoring and management tools to provide information about the utilization of the network and verify the compliance of service level agreements. However, the use of these techniques in other applications and network environments is limited by the convergence time, accuracy, and amount of overhead that they introduce. In this paper, we propose a Hidden Markov Model-based technique to end-to-end available bandwidth estimation and monitoring that improves these performance metrics and therefore promises to expand the use of these techniques in other scenarios. The estimator, which has been implemented in a new tool called Traceband, is as accurate as Spruce and Pathload but considerably faster, and introduce far less overhead. In addition, when compared using bursty cross-traffic, Traceband is the only tool that accurately reacts to zero-traffic periods, which may be particularly useful for those applications that need to make decisions in real time.