Predicting Network Traffic Using TCP Anomalies

Accurately predicting network traffic volume is beneficial for congestion control, improving routing, allocating network resources and network optimization. Traffic congestion happens when a network device is receiving more data packets than its processing capability. The number of retransmissions per flow, packet duplication and synthetic reordering can seriously degrade the overall TCP performance. An unsupervised/supervised technique to accurately identify TCP anomalies occurring during file transfers based on passive measurements of TCP traffic collected using Tstat is proposed. This method will be validated on real large datasets collected from several data transfer nodes. The preliminary results indicate that the percentage of TCP anomalies correlate well with the average throughput in any given time window