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5G radio access network (RAN) slicing aims to logically split aninfrastructure into a set of self-contained programmable RAN slices, with eachslice built on top of the underlying physical RAN (substrate) is a separatelogical mobile network, which delivers a set of services with similarcharacteristics. Each RAN slice is constituted by various virtual networkfunctions (VNFs) distributed geographically in numerous substrate nodes. A keychallenge in building a robust RAN slicing is, therefore, designing a RANslicing (RS)-configuration scheme that can utilize information such as resourceavailability in substrate networks as well as the interdependent relationshipsamong slices to map (embed) VNFs onto live substrate nodes. With suchmotivation, we propose a machine-learning-powered RAN slicing scheme that aimsto accommodate maximum numbers of slices (a set of connected Virtual NetworkFunctions - VNFs) within a given request set. More specifically, we present adeep reinforcement scheme that is called Deep Allocation Agent (DAA). In short,DAA utilizes an empirically designed deep neural network that observes thecurrent states of the substrate network and the requested slices to schedulethe slices of which VNFs are then mapped to substrate nodes using anoptimization algorithm. DAA is trained towards the goal of maximizing thenumber of accommodated slices in the given set by using an explicitly designedreward function. Our experiment study shows that, on average, DAA is able tomaintain a rate of successfully routed slices above 80% in a resource-limitedsubstrate network, and about 60% in extreme conditions, i.e., the availableresources are much less than the demands.

Efficient Embedding VNFs in 5G Network Slicing: A Deep Reinforcement Learning Approach