  Current networks are increasingly growing in size and complexity and so is
the amount of monitoring data that they produce. As a result, network
monitoring systems have to scale accordingly. As a possible approach,
horizontal scalability can be achieved by large data centralization systems
based on clusters, which are expensive and difficult to deploy in a real
production scenario. In this paper we propose and evaluate a series of
methodologies, deployed in real industrial production environments, for network
monitoring and management, from the architecture design to the visualization
system as well as for the anomaly detection methodologies, that intend to
squeeze the vertical resources and overcome the difficulties of data collection
and centralization.
