Safe and Fast Reinforcement Learning for Network Slicing Resource Allocation

Network slicing enables operators to virtually partition network resources and instantiate different virtual networks, supporting flexible quality of service, in the form of service level agreements (SLAs). Optimizing resource allocation for network slicing is a complex task, given the dynamicity and randomness of network conditions. Leveraging principles of reinforcement learning, we propose an algorithmic solution to optimize the SLA success rate across a radio access network. Tailoring the algorithm to the problem at hand, and leveraging some prior knowledge on the system to be optimized, we ensure fast convergence, data efficiency, and safe exploration. The solution is scalable both in the number of cells and in the number of slices. Extensive numerical evaluations on a simulated environment show the effectiveness of the proposed solution and its advantages versus both simple baselines and sophisticated solutions based on deep reinforcement learning, in terms of speed of convergence and SLA success rate.