Future 6G networks are expected to meet extreme Quality-of-Service (QoS) requirements. From delays below one hundred microseconds to bitrates above ten gigabit per second. Besides, these requirements must be guaranteed in massive scenarios, with densities above ten million devices per square kilometer, and be compatible with intense mobility where speed can reach up to one thousand kilometers per hour. In this context, network resource management cannot be static. Any fixed network configuration meeting such extreme requirements should be designed for the worst case, and then it would be oversized and non-profitable. To facilitate a tailored and efficient network resource distribution, dynamic management techniques are needed; so 6G nodes can envision the upcoming needs and get adapted to provide the expected QoS. However, previously reported predictive models for network resource management are either exclusively focused on software instances and service availability, or they consider network resources are a static pool to be optimally distributed among devices and/or verticals. New dynamic management schemes are required, centered on devices and their specific characteristics (high density, mobility, privacy restrictions, etc.), as well as they can predict the number of needed resources to serve the upcoming demand. This paper fills this gap. We propose a predictive management algorithm which can calculate the probability of network congestion for a given amount of resources, using the traffic theory and Gaussian models. Those models are generated through a federated scheme, where base stations periodically monitor the devices’ resource consumption and produce a partial model with additive noise to preserve the devices’ privacy. The network core collects all partial models and uses clustering technologies to produce a time-variant global model describing the dynamic resource demand. Scheduling policies are implemented to ensure the management algorithms do not have a relevant impact on network operations. An experimental validation based on simulation tools is also provided. Results show the achieved prediction precision is close to 93%, and the network resource consumption reduces up to 26% compared to a static configuration.
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