  Network functions virtualization (NFV) enables telecommunications service
providers to realize various network services by flexibly combining multiple
virtual network functions (VNFs). To provide such services, an NFV control
method should optimally allocate such VNFs into physical networks and servers
by taking account of the combination(s) of objective functions and constraints
for each metric defined for each VNF type, e.g., VNF placements and routes
between the VNFs. The NFV control method should also be extendable for adding
new metrics or changing the combination of metrics. One approach for NFV
control to optimize allocations is to construct an algorithm that
simultaneously solves the combined optimization problem. However, this approach
is not extendable because the problem needs to be reformulated every time a new
metric is added or a combination of metrics is changed. Another approach
involves using an extendable network-control architecture that coordinates
multiple control algorithms specified for individual metrics. However, to the
best of our knowledge, no method has been developed that can optimize
allocations through this kind of coordination. In this paper, we propose an
extendable NFV-integrated control method by coordinating multiple control
algorithms. We also propose an efficient coordination algorithm based on
reinforcement learning. Finally, we evaluate the effectiveness of the proposed
method through simulations.
