# ARMONIA: A Unified Access and Metro Network Architecture
## 3 ARMONIA Algorithmic Mechanisms
### Network Slice Requirements Prediction  
The evolution of 5G networks marked the introduction of network slicing. Network slicing embeds different logical networks on the same physical network infrastructure. Each network slice is an isolated end-to-end network that leverages a subset of the physical network resources. Each slice is tailored for diverse and stringent application requirements, in terms of latency, capacity, and other parameters. A number of previous works focused on the task of resource allocation for network slicing [26; 27; 28]. More recently dynamic network slicing has received increased attention [29; 30; 31; 32; 33; 34]. A basic idea is to leverage a traffic predictor to estimate the future demands of the slices in order to perform dynamic resource allocation. Different timeframes can be selected for the prediction--e.g., over hours or days--depending on its purpose. A dynamic slicing management during the day requires a mechanism that can predict the traffic volume of the next e.g., few hours. The traffic prediction in this timeframe could be useful in scenarios of large volume variations e.g., during a popular athletic event, resulting in efficient resource allocation. The continuous slice adaptation during the day is however questionable: on the one hand, the network will be able to continuously adapt to the current conditions. On the other hand, the continuous parameter adaptation will increase the network management burden and even lead to misconfigurations. Another solution could be to perform traffic forecasting on longer timeframes and slowly adapt the network to the predicted conditions. This would incur a lower control plane overhead, but would result in a higher resource overprovisioning compared to the first alternative. In any case, traffic forecasting in longer timeframes is still better than static overprovisioning or dynamic overprovisioning, where no traffic prediction is performed, but the resource allocation is adapted based on static or dynamic rough estimates of the required resources. In our work, we consider a traffic prediction algorithm that can estimate the requirements of a slice in a daily timeframe. More specifically, we assumed that the daily traffic volume consists of discrete values of traffic volume per, e.g., 10 min. The traffic prediction algorithm takes as input the traffic volume of a number of previous days, and outputs the prediction for the whole following day. We assume that this process is executed on a daily basis. The predicted traffic volume for each slice is then given to the resource allocation algorithm to plan the network for the respective demands. The literature on short-term traffic forecasting covers a broad spectrum of research areas, including linear and non-linear methods [35; 36; 37; 38]. Linear mechanisms include statistical approaches such as ARIMA and Prophet, whereas non-linear include machine learning algorithms such as multilayer perceptrons (MLPs) and recurrent neural networks (RNNs). In this work, we considered two different traffic prediction algorithms, the first is based on long short-term memory networks (LSTMs) [35], and the second on Prophet [36]. LSTMs are a type of RNNs. In conventional neural networks the adjacent layers may be fully connected, but there are no connections between the nodes of a given layer. RNNs have hidden states by which previous outputs can be used as inputs. In this way, the relationship between current and past input can be modeled. However, if a sequence is long enough, they tend to forget the information from earlier time steps; this is known as the vanishing gradient problem. LSTMs have a hidden layer that acts as memory. This layer can capture short- and long-term correlations in the time domain. Prophet is an automatic time series forecasting algorithm that is based on the idea of fitting generalized additive models. The three main model components are trend, seasonality and holidays.