In 5G networks, it is necessary to provide services while meeting various service requirements, such as high data rates and low latency, in response to dynamic network conditions. Multi-access edge computing (MEC) is a promising concept to meet these requirements. The MEC environment enables service providers to deploy their low latency services that are composed of multiple components. However, operating a service manually and attempting to satisfy the quality of service (QoS) requirements is difficult because many factors need to be considered in an MEC scenario. In this paper, we propose an auto-scaling method using deep Q-networks (DQN), which is a reinforcement learning algorithm, to resize the number of instances assigned to service. In our evaluation, compared to other baseline methods, the proposed approach maintains the appropriate number of instances effectively in response to dynamic traffic change while satisfying QoS and minimizing the cost of operating the service in the MEC environment. The proposed method was implemented as a module running in OpenStack and published as open-source software.

中文翻译：

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多路访问边缘计算环境下基于深度Q网络的服务自动伸缩

在5G网络中，需要在提供服务的同时满足各种业务需求，如高数据速率和低时延，以应对动态的网络状况。多接入边缘计算 (MEC) 是满足这些要求的有前途的概念。MEC 环境使服务提供商能够部署由多个组件组成的低延迟服务。然而，手动操作服务并试图满足服务质量（QoS）要求是困难的，因为在 MEC 场景中需要考虑许多因素。在本文中，我们提出了一种使用深度 Q 网络 (DQN) 的自动缩放方法，这是一种强化学习算法，用于调整分配给服务的实例数量。在我们的评估中，与其他基线方法相比，所提出的方法有效地维持适当数量的实例以响应动态流量变化，同时满足 QoS 并最小化在 MEC 环境中运行服务的成本。所提出的方法作为在 OpenStack 中运行的模块来实现，并作为开源软件发布。