5G wireless network technology will not only significantly increase bandwidth but also introduce new features such as mMTC and URLLC. However, high request latency will remain a challenging problem even with 5G due to the massive requests generated by an increasing number of devices that require long travel distances to the services deployed in cloud centers. By pushing the services closer to the edge of the network, edge computing is recognized as a promising technology to reduce latency. However, properly deploying services among resource-constrained edge servers is an unsolved problem. In this article, we propose a deep reinforcement learning approach to preferably deploy the services to the edge servers with consideration of the request patterns and resource constraints of users, which have not been adequately explored. First, the system model and optimization objectives are formulated and investigated. Then the problem is modeled as a Markov decision process and solved using the Dueling-Deep Q-network algorithm. The experimental results, based on the evaluation of real-life mobile wireless datasets, show that this reinforcement learning approach could be applied to patterns of requests and improve performance.

中文翻译：

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借助请求感知调度实现基于增强学习的5G移动边缘计算服务部署

5G无线网络技术不仅将显着增加带宽，而且还将引入新功能，例如mMTC和URLLC。但是，由于越来越多的设备需要大量的距离才能到达部署在云中心的服务，因此即使使用5G，高请求延迟仍将是一个具有挑战性的问题。通过将服务推向更靠近网络边缘，边缘计算被认为是减少等待时间的有前途的技术。但是，在资源受限的边缘服务器之间正确部署服务是一个尚未解决的问题。在本文中，我们提出了一种深度强化学习方法，以考虑到用户的请求模式和资源约束，从而将服务最好地部署到边缘服务器，而这尚未得到充分探讨。第一，制定并研究了系统模型和优化目标。然后将该问题建模为马尔可夫决策过程，并使用“决斗-深层Q网络”算法解决。基于对现实生活中的移动无线数据集的评估，实验结果表明，这种强化学习方法可以应用于请求模式并提高性能。