Abstract: Nowadays, a paradigm shift in mobile computing has been introduced by the ever-increasing heterogenous terminal devices, from the centralized mobile cloud towards the mobile edge. Multi-access edge computing (MEC) emerges as a promising ecosystem to support multi-service and multi-tenancy. It takes advantage of both mobile computing and wireless communication technologies for cloud-network integration. However, the physical hardware constraints of the terminal devices, along with the limited connection capacity of the wireless channel pose numerous challenges for cloud-network integration. The incapability of control over all the possible resources (e.g., computation, communication, cache) becomes the main hurdle of realizing delay-sensitive and real time services. To break this stalemate, this article investigates a software-defined fine-grained multi-access architecture, which takes full control of the computation and communication resources. We further investigate a Q-Learning based two-stage resource allocation strategy to better cater the heterogenous radio environments and various user requirements. We discuss the feasibility of the proposed architecture and demonstrate its effectiveness through extensive simulations.

Key words: cloud-network integration, multi-access edge computing (MEC), fine-grained access network, software-defined network, deep reinforcement learning