Accurate capacity planning is essential to ensure uninterrupted services and network stability through peak hours for the transport core network of KPN. This involves a trade-off between minimizing the risks of capacity shortages and costs of capacity expansions. High network loads are occurring more frequently and their magnitude is increasing. This necessitates measures to foresee high load situations before network capacity is surpassed. Currently, planning is based on manual predictions that lack substantiation. This research aims to improve network capacity planning by development of a forecast for the next year.
An analysis of the daily maximum traffic data of the transport core is performed, to determine the most suitable models for the prediction of network traffic. The data analysis, employing time series decomposition, revealed non-stationary trends and annual seasonality; traffic decreases throughout the summer and increases in the winter. An upward trend in the frequency and intensity of traffic peaks, highlights the growing demand and shifts in usage behavior. The extreme traffic peaks in the historical data were correlated to F1 race days and other anticipated events.
Two algorithms that integrate exogenous variables were assessed to predict the extreme values. The models either yielded inaccurate traffic predictions or encountered challenges in interpretability and pattern recognition, with the limited amount of data available. In response to these limitations, a decomposed forecast was created that predicts the trend and seasonality. Furthermore, Extreme Value Analysis (EVA) was implemented to address the extreme values in the data.
The final prediction framework combines the decomposed forecast with EVA for the next six quarters and outperforms the other models. The model effectively captures extreme values and provides insights into the maximum expected peaks and risk levels. The substantiated forecasts of the EVA model and the manual predictions yielded comparable results. However, the EVA model provides better insights into the likelihood of exceeding specific traffic values, which enhances capacity calculations and precision.
The prediction framework has been integrated into the business interface of KPN, which marks the initial step in the automatization of short-term capacity planning. The research insights emphasize the intricate nature of accurate prediction of future demand and advocate for scalable solutions beyond building new capacity. These solutions range from short-term mitigation to long-term strategies designed to alleviate high network loads. They underscore the importance of the implementation and integration of
dynamic decision-making within a digital twin of the network to ensure sustained effectiveness.

This thesis’ research concerns the time-dynamics of a complex geographical network of municipalities, i.e. the Dutch Municipality Network over the period 1830-2019. By analysing 190 years of socioeconomic statistical data and applying contemporary tooling from network science and geographic information systems (GIS), the findings from this research can provide a new approach and supportive methods for policymakers, statistical offices, researchers and businesses (to decide when and where to invest).

Multi-access Edge Computing (MEC) is a concept brought up by ETSI and it places computing, storage, processing and network resources into MEC hosts and places these MEC hosts as close as needed to the telecom network edge in order to reduce service latency and bandwidth usage. For self-driving vehicles, streaming video and real-time gaming, the devices involved (e.g. vehicles, cellphones, etc.) might not have enough capabilities to perform all the computations and might not have sufficient storage capacity; MEC can be used here for offloading data computations and content caching. To enhance service quality and user experience, MEC hosts and MEC applications should be located close(r) to the end-users, which increases the number of handovers between MEC hosts to maintain MEC service continuity for mobile end-users as well as the costs for the telecom operators. Therefore, a balance needs to be found. Consider the fact that mobile UEs need MEC service handovers to maintain service continuity and handovers may cause service interruptions which can cause severe degradation to MEC service qualities and user experience, hence the number of handovers between MEC hosts experienced by end-users should be minimized. To find a suitable deployment of MEC hosts and MEC applications in order to minimize the number of handovers, three greedy algorithms and two heuristic algorithms are introduced, implemented, tested, compared and analyzed in this thesis to see which identifies the deployment mechanism that has the smallest number of handovers. When it is time for a mobile UE to connect to a new MEC host and there are multiple potential choices of the new MEC host, the most suitable one for the UE needs to be determined dynamically according to the real-time condition of each possible MEC host. To achieve this, reinforcement learning is considered. Three different reinforcement learning algorithms based on SARSA learning and Deep Q Network are introduced, implemented, tested, compared and analyzed in this thesis. Furthermore, a decision-making mechanism is designed to cope with exceptional situations where the required service quality cannot be guaranteed.