  The traditional Internet has encountered a bottleneck in allocating network
resources for emerging technology needs. Network virtualization (NV) technology
as a future network architecture, the virtual network embedding (VNE) algorithm
it supports shows great potential in solving resource allocation problems.
Combined with the efficient machine learning (ML) algorithm, a neural network
model close to the substrate network environment is constructed to train the
reinforcement learning agent. This paper proposes a two-stage VNE algorithm
based on deep reinforcement learning (DRL) (TS-DRL-VNE) for the problem that
the mapping result of existing heuristic algorithm is easy to converge to the
local optimal solution. For the problem that the existing VNE algorithm based
on ML often ignores the importance of substrate network representation and
training mode, a DRL VNE algorithm based on full attribute matrix (FAM-DRL-VNE)
is proposed. In view of the problem that the existing VNE algorithm often
ignores the underlying resource changes between virtual network requests, a DRL
VNE algorithm based on matrix perturbation theory (MPT-DRL-VNE) is proposed.
Experimental results show that the above algorithm is superior to other
algorithms.
