  Network Slice placement with the problem of allocation of resources from a
virtualized substrate network is an optimization problem which can be
formulated as a multiobjective Integer Linear Programming (ILP) problem.
However, to cope with the complexity of such a continuous task and seeking for
optimality and automation, the use of Machine Learning (ML) techniques appear
as a promising approach. We introduce a hybrid placement solution based on Deep
Reinforcement Learning (DRL) and a dedicated optimization heuristic based on
the Power of Two Choices principle. The DRL algorithm uses the so-called
Asynchronous Advantage Actor Critic (A3C) algorithm for fast learning, and
Graph Convolutional Networks (GCN) to automate feature extraction from the
physical substrate network. The proposed Heuristically-Assisted DRL (HA-DRL)
allows to accelerate the learning process and gain in resource usage when
compared against other state-of-the-art approaches as the evaluation results
evidence.
