End-to-end pattern classification based congestion detection using SVM

摘要

Because packets dropped due to network congestion cannot reach the intended receiver whereas corrupted packets can still be received, the reception status of multiple packets is different for congested and non-congested paths. This difference reflects a spatial variation in the received data stream that is indicative of congestion. Network congestion detection is described that treats the reception status of sequences of multiple packets as patterns and converts the problem of congestion detection into a two-class pattern classification problem. A Support Vector Machine (SVM) classifier is trained to classify the reception status of sequences of packets as being indicative or not of network congestion. If network congestion is detected, congestion control measures can then be taken. Extensive simulations demonstrate high detection accuracy under different network parameters.