# ARMONIA: A Unified Access and Metro Network Architecture
## 4 Simulation Results
### Traffic Prediction  
We also evaluated the accuracy of the traffic predictor. The implemented LSTM architecture consists of a three-layer structure. The LSTM input layer includes 200 hidden units and is followed by two dense fully connected layers with 160 and 144 hidden units, respectively. LeakyReLU was used (with alpha = 0.1) as activation function across all layers for computational simplicity, while the stochastic gradient descent (SGD) algorithm was used as optimizer. Moreover, mean squared error (MSE) was used as loss function. In the Prophet model, all types of seasonalities that are added to the model, are estimated using a partial Fourier sum. The implemented model incorporates trend and daily seasonality, since sub-daily data are used. No additional seasonalities and holiday effects were considered in this schema. In order to prevent overfit in terms of daily seasonality, the default Fourier order of 3 was used. Furthermore, to imprint a more stable trend the _changepoint_prior_scale_ argument was set to 0.001 since trend changes were being overfit. We produced the training and test data as described in [40]: we summed four sin time series with different amplitude and time shift to generate a traffic profile that resembles a daily traffic pattern. We also added Gaussian noise with zero mean and standard deviation of 0.05 to account for random irregularities. We considered that each day consists of 10-min discrete traffic values. The algorithms take as input the traffic volume of three days and output the prediction for the next day. In both algorithms, we considered the root relative squared error (RRSE) as our evaluation metric. The RRSE is the scaled version of the root-mean-square error (RMSE). The RRSE is an accuracy metric that does not depend on the data scale (i.e., the scale of the vertical axis's values). Prior to fitting LSTM, we applied the following preprocessing strategy on the training data: a standard scaler was fitted to the data and afterwards the dataset was split in training and test set at a ratio of 75%/25% respectively. The model was trained for 20 epochs and batch size was set to 16 considering the input size. Prophet is a statistical model and there is no need for scaling the data. As with LSTM, the dataset was split in training and test at the same ratio. The RRSE of the LSTM was 0.083, and that of the Prophet was 0.047. The accuracy of the Prophet means that its predicted values differ approximately 5% from the actual ones. These results demonstrate that the traffic prediction accuracy is high and can be used for resource allocation purposes. Moreover, to take into account the inaccuracy of the prediction, we assumed an overprovision margin that is considered during the resource allocation. Figure 8 presents the utilization of transponders and spectrum for the different traffic loads and for the different resource allocation scenarios. As expected, the actual provisioning achieves the lowest resource utilization. The ML assisted provisioning is close, with a difference that varies up to 7%,  
Figure 7: Signal-to-Noise Ratio (SNR) accuracy of the QoT estimator for various number of lightpaths (80, 120, 160). while for some loads (T = 35 Tbps for transponder utilization and T = 27 Tbps for spectrum utilization) it is almost identical to the actual provisioning scenario. This is due to the fact that the transponder allocation depends on the selected transmission configurations. As the number and size of slicing requests change, the transponders are dynamically reconfigured and are able to serve the higher networking demands without utilizing extra ones. The static resource allocation, which allocates the resources statically based on the highest expected traffic, achieves the worst performance and allocates up to 15% more transponders and 11% more spectrum. We then examined the average utilization of the processing nodes of the different network layers. As Figure 9 illustrates, the utilization between the actual and the ML assisted provisioning scenarios is close.