CLARA: A Constrained Reinforcement Learning Based Resource Allocation Framework for Network Slicing
This addresses the problem of flexible resource utilization for mobile network operators, but it is incremental as it builds on existing reinforcement learning methods for a specific domain.
The paper tackles dynamic resource allocation for network slicing in 5G networks by formulating it as a Constrained Markov Decision Process and proposing CLARA, a constrained reinforcement learning algorithm, which outperforms baselines in resource allocation with service demand guarantees.
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.