Towards a bayesian statistical model for the classification of the causes of data loss
HPCC'05 Proceedings of the First international conference on High Performance Computing and Communications
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A necessary step in the development of next-generation congestion control mechanisms is the ability to accurately classify the root cause(s) of observed data loss and to develop responses tailored to the particular cause. Toward this end, we are developing a classification mechanism based on the collection and analysis of what we term packet-loss signatures, which describe the patterns of packet loss in the current transmission window. We are exploring the application of complexity theory to the problem of learning the underlying structure (or lack thereof) of these signatures, and studying the relationship between such underlying structure and the system conditions responsible for its generation. In this paper, we describe the algorithm for determining the complexity of packet-loss signatures, show how complexity measures can be mapped to the underlying causes of packet loss, and provide experimental results demonstrating the effectiveness of our approach.