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Service Overlay Networks (SON) can offer end to end Quality of Service by leasing bandwidth from Internet Autonomous Systems. To maximize profit, the SON can continually adapt its leased bandwidth to traffic demand dynamics based on online traffic trend estimation. In this paper, we propose novel approaches for online traffic trend estimation that fits the SON capacity adaptation. In the first approach, the smoothing parameter of the exponential smoothing (ES) model is adapted to traffic trend. Here, the trend is estimated using measured connection arrival rate autocorrelation or cumulative distribution functions. The second approach applies Kalman filter whose model is built from historical traffic data. In this case, availability of the estimation error distribution allows for better control of the network Grade of Service. Numerical study shows that the proposed autocorrelation based ES approach gives the best combined estimation response-stability performance when compared to known ES methods. The proposed Kalman filter based approach improves further the capacity adaptation performance by limiting the increase of connection blocking when traffic level is increasing.