Model-based Loss Inference by TCP over Heterogeneous Networks

The Transmission Control Protocol (TCP) has been the protocol of choice for many Internet applications requiring reliable connections. The design of TCP has been challenged by the extension of connections over wireless links. In this paper, we investigate a Bayesian approach to infer at the source host the reason of a packet loss, whether congestion or wireless transmission error. Our approach is “mostly” end-to-end since it requires only one long-term average quantity (namely, long-term average packet loss probability over the wireless segment) that may be best obtained with help from the network (e.g. wireless access agent). Specifically, we use Maximum Likelihood Ratio tests to evaluate TCP as a classifier of the type of packet loss. We study the effectiveness of short-term classification of packet errors (congestion vs. wireless), given stationary prior error probabilities and distributions of packet delays conditioned on the type of packet loss (measured over a longer time scale). Using our Bayesian-based approach and extensive simulations, we demonstrate that an efficient online error classifier can be built as long as congestion-induced losses and losses due to wireless transmission errors produce sufficiently different statistics. We introduce a simple queueing model to underline the conditional delay distributions arising from different kinds of packet losses over a heterogeneous wired/wireless path. To infer conditional delay distributions, we consider Hidden Markov Model (HMM) which explicitly considers discretized delay values observed by TCP as part of its state definition, in addition to an HMM which does not as in [9]. We demonstrate how estimation accuracy is influenced by different proportions of congestion versus wireless losses and penalties on incorrect classification.