EdgeSlice: Slicing Wireless Edge Computing Network with Decentralized Deep Reinforcement Learning
EdgeSlice: Slicing Wireless Edge Computing Network with Decentralized Deep Reinforcement Learning
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DOI：
10.1109/icdcs47774.2020.00028
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发表时间：
2020-03
期刊：
影响因子：
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通讯作者：
Qiang Liu;T. Han;Ephraim Moges
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文献类型：
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作者：
Qiang Liu;T. Han;Ephraim Moges
5G和边缘计算将服务于各种新兴的用例，这些用例对多种资源有不同的要求，例如无线电、交通和计算。网络切片是一种很有前途的技术，可以创建可以根据不同用例的需求进行定制的虚拟网络。调配网络切片需要端到端的资源协调，这很有挑战性。本文设计了一个支持端到端网络动态切片的分布式资源编排系统EdgeSlice。EdgeSlice引入了一种新的去中心化深度强化学习(D-DRL)方法来高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级别协议(SLA)。编排代理学习网络切片的资源需求，并相应地编排资源分配，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，以支持在运行时动态配置端到端资源。利用OpenAirInterfaceLTE网络、OpenDayLight SDN交换机和CUDA GPU平台，在一个端到端的无线边缘计算网络原型上实现了EdgeSlice。通过实验和轨迹驱动的模拟对EdgeSlice的性能进行了评估。评估结果表明，EdgeSlice在性能、可伸缩性、兼容性方面都比基线有了很大的提高。
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.