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.

中文翻译：

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EdgeSlice：使用去中心化深度强化学习对无线边缘计算网络进行切片

5G 和边缘计算将服务于对多种资源有不同需求的各种新兴用例，例如无线电、交通和计算。网络切片是一种很有前途的技术，用于创建可以根据不同用例的要求进行定制的虚拟网络。供应网络切片需要端到端的资源编排，这具有挑战性。在本文中，我们设计了一个名为 EdgeSlice 的去中心化资源编排系统，用于动态端到端网络切片。EdgeSlice 引入了一种新的分散式深度强化学习 (D-DRL) 方法来有效地编排端到端资源。D-DRL 由一个性能协调器和多个编排代理组成。性能协调器管理所有编排代理中的资源编排策略，以确保网络切片的服务水平协议（SLA）。编排代理学习网络切片的资源需求，并据此编排资源分配，以在受限的网络和计算资源下优化切片的性能。我们设计了无线电、传输和计算管理器，以在运行时实现端到端资源的动态配置。我们在具有 OpenAirInterface LTE 网络、OpenDayLight SDN 交换机和 CUDA GPU 平台的端到端无线边缘计算网络原型上实施 EdgeSlice。EdgeSlice 的性能是通过实验和跟踪驱动的模拟来评估的。