Service migration is an often-used approach in cloud computing to minimize the access cost by moving the service close to most users. Although it is effective in a certain sense, the service migration in existing research still suffers from some deficiencies in its evolutionary abilities in scalability , sensitivity , and adaptability to effectively react to the dynamically changing environments. This article proposes an evolutionary framework based on deep reinforcement learning for virtual service migration in large-scale mobile cloud centers. To enhance the spatio-temporal sensitivity of the algorithm, we design a scalable reward function for virtual service migration, redefine the input state, and add a Recurrent Neural Network ( RNN ) to the learning framework. Additionally, in order to enhance the adaptability of the algorithm, we also decompose the action space and exploit the network cost to adjust the number of virtual machine (VMs). The experimental results show that, compared with the existing results, the migration strategy generated by the algorithm can not only significantly reduce the total service cost and achieve the load balancing at the same time, but also address the burst situations with low cost in dynamic environments.

中文翻译：

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SMig-RL

服务迁移是云计算中常用的一种方法，通过将服务移近大多数用户来最小化访问成本。虽然在一定意义上是有效的，但现有研究中的服务迁移在其进化能力上仍然存在一些不足。可扩展性,灵敏度， 和适应性有效应对动态变化的环境。本文提出了一种基于深度强化学习的进化框架，用于大规模移动云中心的虚拟服务迁移。为了增强算法的时空敏感性，我们为虚拟服务迁移设计了一个可扩展的奖励函数，重新定义了输入状态，并添加了一个循环神经网络（循环神经网络) 到学习框架。此外，为了增强算法的适应性，我们还分解了动作空间并利用网络成本来调整虚拟机（VM）的数量。实验结果表明，与现有结果相比，该算法生成的迁移策略不仅可以显着降低总服务成本，同时实现负载均衡，而且能够以低成本解决动态环境下的突发情况。 .