  5G and edge computing will serve various emerging use cases that have diverse
requirements of multiple resources, e.g., radio, transportation, and computing.
Network slicing is a promising technology for creating virtual networks that
can be customized according to the requirements of different use cases.
Provisioning network slices requires end-to-end resource orchestration which is
challenging. In this paper, we design a decentralized resource orchestration
system named EdgeSlice for dynamic end-to-end network slicing. EdgeSlice
introduces a new decentralized deep reinforcement learning (D-DRL) method to
efficiently orchestrate end-to-end resources. D-DRL is composed of a
performance coordinator and multiple orchestration agents. The performance
coordinator manages the resource orchestration policies in all the
orchestration agents to ensure the service level agreement (SLA) of network
slices. The orchestration agent learns the resource demands of network slices
and orchestrates the resource allocation accordingly to optimize the
performance of the slices under the constrained networking and computing
resources. We design radio, transport and computing manager to enable dynamic
configuration of end-to-end resources at runtime. We implement EdgeSlice on a
prototype of the end-to-end wireless edge computing network with
OpenAirInterface LTE network, OpenDayLight SDN switches, and CUDA GPU platform.
The performance of EdgeSlice is evaluated through both experiments and
trace-driven simulations. The evaluation results show that EdgeSlice achieves
much improvement as compared to baseline in terms of performance, scalability,
compatibility.
