End-to-End Inference of Loss Nature in a Hybrid Wired/Wireless Environment

Overview & Synopsis

In a hybrid wired/wireless environment, an effective classification technique that identifies the type of a packet loss, i.e., a loss due to wireless link errors or a loss due to congestion, is needed to help a TCP connection take congestion control actions only on congestion-induced losses. Our classification technique is developed based on the loss pairs measurement technique and Hidden Markov Models (HMMs). The intuition is that the delay distribution around wireless losses is different from the one around congestion losses. An HMM can be trained to capture the delays observed around each type of loss by different state(s) in the derived HMM. We develop an automated way to associate a loss type with a state based on the delay features it captures. Thus, classification of a loss can be determined by the loss type associated with the state in which the HMM is at that loss. Simulations confirm the effectiveness of our technique under most network conditions, and its superiority over the Vegas predictor. We identify conditions under which our technique does not perform well.