  Network slicing is a promising technology that allows mobile network
operators to efficiently serve various emerging use cases in 5G. It is
challenging to optimize the utilization of network infrastructures while
guaranteeing the performance of network slices according to service level
agreements (SLAs). To solve this problem, we propose SafeSlicing that
introduces a new constraint-aware deep reinforcement learning (CaDRL) algorithm
to learn the optimal resource orchestration policy within two steps, i.e.,
offline training in a simulated environment and online learning with the real
network system. On optimizing the resource orchestration, we incorporate the
constraints on the statistical performance of slices in the reward function
using Lagrangian multipliers, and solve the Lagrangian relaxed problem via a
policy network. To satisfy the constraints on the system capacity, we design a
constraint network to map the latent actions generated from the policy network
to the orchestration actions such that the total resources allocated to network
slices do not exceed the system capacity. We prototype SafeSlicing on an
end-to-end testbed developed by using OpenAirInterface LTE, OpenDayLight-based
SDN, and CUDA GPU computing platform. The experimental results show that
SafeSlicing reduces more than 20% resource usage while meeting SLAs of network
slices as compared with other solutions.
