RL-NSB: Reinforcement Learning-Based5G Network Slice Broker
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Network slicing is considered one of the mainpillars of the upcoming 5G networks. Indeed, the ability toslice a mobile network and tailor each slice to the needs ofthe corresponding tenant is envisioned as a key enabler forthe design of future networks. However, this novel paradigmopens up to new challenges, such as isolation between networkslices, the allocation of resources across them, and the admissionof resource requests by network slice tenants. In this paper,we address this problem by designing the following buildingblocks for supporting network slicing: i) traffic and user mobil-ity analysis, ii) a learning and forecasting scheme per slice,iii) optimal admission control decisions based on spatial andtraffic information, and iv) a reinforcement process to drivethe system towards optimal states. In our framework, namelyRL-NSB, infrastructure providers perform admission controlconsidering the service level agreements (SLA) of the differenttenants as well as their traffic usage and user distribution, andenhance the overall process by the means of learning and thereinforcement techniques that consider heterogeneous mobilityand traffic models among diverse slices. Our results show that byrelying on appropriately tuned forecasting schemes, our approachprovides very substantial potential gains in terms of systemutilization while meeting the tenants’ SLAs.