  One of the key benefits of virtualized radio access networks (vRANs) is
network management flexibility. However, this versatility raises
previously-unseen network management challenges. In this paper, a
learning-based zero-touch vRAN orchestration framework (LOFV) is proposed to
jointly select the functional splits and allocate the virtualized resources to
minimize the long-term management cost. First, testbed measurements of the
behaviour between the users' demand and the virtualized resource utilization
are collected using a centralized RAN system. The collected data reveals that
there are non-linear and non-monotonic relationships between demand and
resource utilization. Then, a comprehensive cost model is proposed that takes
resource overprovisioning, declined demand, instantiation and reconfiguration
into account. Moreover, the proposed cost model also captures different routing
and computing costs for each split. Motivated by our measurement insights and
cost model, LOFV is developed using a model-free reinforcement learning
paradigm. The proposed solution is constructed from a combination of deep
Q-learning and a regression-based neural network that maps the network state
and users' demand into split and resource control decisions. Our numerical
evaluations show that LOFV can offer cost savings by up to 69\% of the optimal
static policy and 45\% of the optimal fully dynamic policy.
