Multi-Tenant Cross-Slice Resource Orchestration: A Deep Reinforcement Learning Approach

07/17/2018
by   Xianfu Chen, et al.
0

In a software-defined radio access network (RAN), a major challenge lies in how to support diverse services for mobile users (MUs) over a common physical network infrastructure. Network slicing is a promising solution to tailor the network to match such service requests. This paper considers a software-defined RAN, where a limited number of channels are auctioned across scheduling slots to MUs of multiple service providers (SPs) (i.e., the tenants). Each SP behaves selfishly to maximize the expected long-term payoff from competition with other SPs for the orchestration of channel access opportunities over its MUs, which request both mobile-edge computing and traditional cellular services in the slices. This problem is modelled as a stochastic game, in which the decision makings of a SP depend on the network dynamics as well as the control policies of its competitors. We propose an abstract stochastic game to approximate the Nash equilibrium. The selfish behaviours of a SP can then be characterized by a single-agent Markov decision process (MDP). To simplify decision makings, we linearly decompose the per-SP MDP and derive an online scheme based on deep reinforcement learning to approach the optimal abstract control policies. Numerical experiments show significant performance gains from our scheme.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset