  As mobile networks proliferate, we are experiencing a strong diversification
of services, which requires greater flexibility from the existing network.
Network slicing is proposed as a promising solution for resource utilization in
5G and future networks to address this dire need. In network slicing, dynamic
resource orchestration and network slice management are crucial for maximizing
resource utilization. Unfortunately, this process is too complex for
traditional approaches to be effective due to a lack of accurate models and
dynamic hidden structures. We formulate the problem as a Constrained Markov
Decision Process (CMDP) without knowing models and hidden structures.
Additionally, we propose to solve the problem using CLARA, a Constrained
reinforcement LeArning based Resource Allocation algorithm. In particular, we
analyze cumulative and instantaneous constraints using adaptive interior-point
policy optimization and projection layer, respectively. Evaluations show that
CLARA clearly outperforms baselines in resource allocation with service demand
guarantees.
