Virtual private network bandwidth management with traffic prediction

Dynamic link resizing is an attractive approach for resource management in virtual private networks (VPNs) serving modern real-time and multimedia traffic. In this paper, we assess the use of linear traffic predictors to dynamically resize the bandwidth of VPN links. We present the results of performance comparisons of three predictors: Gaussian, auto-regressive moving average (ARMA) and fractional auto-regressive integrated moving average (fARIMA). The comparisons are based on the mean packet delay, the variance of the packet delay, and the buffer requirements. Guided by our performance tests, we propose and evaluate a new predictor for link resizing: linear predictor with dynamic error compensation (L-PREDEC). Our performance tests show that L-PREDEC works better than Gaussian, ARMA and fARIMA in terms of the three metrics listed above. The benefit of L-PREDEC over the Gaussian predictor is demonstrated in two configurations: a common queue with aggregate link resizing and multiple queues with separate link resizing. In both configurations, L-PREDEC has consistently achieved better multiplexing gain and higher bandwidth utilization than its Gaussian counterpart.