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In this paper we evaluate four different metrics for non intrusive bottleneck detection based on TCP counters. This work is based on the full TCP statistics recorded on five days spread over the last one and a half year within the core network of a mobile network operator in Austria. Scatterplots, so called ldquofootprintsrdquo, were generated counting the number of packets and the number of retransmission for each user during the peak hours. Two of the datasets had a known capacity bottleneck in place. Based on those datasets we benchmarked the different metrics for the detection of a bottleneck event. We preprocessed the traces in order to remove the traffic increase. After this step all metrics were able to detect the special bottleneck case. Even traces separated for more than one year deliver a clear result. The performance of a PSNR metric was similar to the other metrics based on more sophisticated functions.

Analysis and Detection of Bottlenecks via TCP Footprints in live 3G Networks