  Effective network slicing requires an infrastructure/network provider to deal
with the uncertain demand and real-time dynamics of network resource requests.
Another challenge is the combinatorial optimization of numerous resources,
e.g., radio, computing, and storage. This article develops an optimal and fast
real-time resource slicing framework that maximizes the long-term return of the
network provider while taking into account the uncertainty of resource demand
from tenants. Specifically, we first propose a novel system model which enables
the network provider to effectively slice various types of resources to
different classes of users under separate virtual slices. We then capture the
real-time arrival of slice requests by a semi-Markov decision process. To
obtain the optimal resource allocation policy under the dynamics of slicing
requests, e.g., uncertain service time and resource demands, a Q-learning
algorithm is often adopted in the literature. However, such an algorithm is
notorious for its slow convergence, especially for problems with large
state/action spaces. This makes Q-learning practically inapplicable to our case
in which multiple resources are simultaneously optimized. To tackle it, we
propose a novel network slicing approach with an advanced deep learning
architecture, called deep dueling that attains the optimal average reward much
faster than the conventional Q-learning algorithm. This property is especially
desirable to cope with real-time resource requests and the dynamic demands of
users. Extensive simulations show that the proposed framework yields up to 40%
higher long-term average return while being few thousand times faster, compared
with state of the art network slicing approaches.
