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

DeepSlicing: Deep Reinforcement Learning Assisted Resource Allocation for Network Slicing