Cellular network nodes should be dynamically switched on/off based on the load requirements of the network, to save power and minimize inter-cell interference. This should be done keeping into account global interference effects, which requires a centralized approach. In this paper, we present an architecture, realized within the Flex5GWare EU project, that manages a large-scale cellular network, switching on and off nodes based on load requirements and context data. We describe the architectural framework and the optimization model that is used to decide the activity state of the nodes. We present simulation results showing that the framework adapts to the minimum power level based on the cell loads.

Computing network paths under worst-case delay constraints has been the subject of abundant literature in the past two decades. Assuming weighted fair queueing scheduling at the nodes, this translates to computing paths and reserving rates at each link. The problem is 𝒩𝒫-hard in general, even for a single path; hence polynomial-time heuristics have been proposed in the past that either assume equal rates at each node, or compute the path heuristically and then allocate the rates optimally on the given path. In this paper we show that the above heuristics, albeit finding optimal solutions quite often, can lead to failing of paths at very low loads, and that this could be avoided by solving the problem, i.e. path computation and rate allocation, jointly at optimality. This is possible by modeling the problem as a mixed-integer second-order cone program and solving it optimally in split-second times for relatively large networks on commodity hardware; this approach can also be easily turned into a heuristic one, trading a negligible increase in blocking probability for one order of magnitude of computation time. Extensive simulations show that these methods are feasible in today's Internet service provider networks and they significantly outperform the existing schemes in terms of blocking probability.

Train Routing is a problem that arises in the early phase of the passenger railway planning process, usually several months before operating the trains. The main goal is to assign each train a stopping platform and the corresponding arrival/departure paths through a railway station. It is also called Train Platforming when referring to the platform assignment task. Railway stations often represent bottlenecks and train delays can easily disrupt the routing schedule. Thereby railway stations are responsible for a large part of the delay propagation in the whole network. In this research we present different models to compute robust routing schedules and we study their power in an online context together with different re-scheduling strategies. We also design a simulation framework and use it to evaluate and compare the effectiveness of the proposed robust models and re-scheduling algorithms using real-world data from Rete Ferroviaria Italiana, the main Italian Railway Infrastructure Manager.