  Network slicing enables multiple virtual networks run on the same physical
infrastructure to support various use cases in 5G and beyond. These use cases,
however, have very diverse network resource demands, e.g., communication and
computation, and various performance metrics such as latency and throughput. To
effectively allocate network resources to slices, we propose DeepSlicing that
integrates the alternating direction method of multipliers (ADMM) and deep
reinforcement learning (DRL). DeepSlicing decomposes the network slicing
problem into a master problem and several slave problems. The master problem is
solved based on convex optimization and the slave problem is handled by DRL
method which learns the optimal resource allocation policy. The performance of
the proposed algorithm is validated through network simulations.
