Recent 5G trials have demonstrated the usefulness of the Network Slicing concept that delivers customizable services to new and under-serviced industry sectors. However, user mobility's impact on the optimal resource allocation within and between slices deserves more attention. Slices and their dedicated resources should be offered where the services are to be consumed to minimize network latency and associated overheads and costs. Different mobility patterns lead to different resource re-allocation triggers, leading eventually to slice mobility when enough resources are to be migrated. The selection of the proper triggers for resource re-allocation and related slice mobility patterns is challenging due to triggers' multiplicity and overlapping nature. In this paper, we investigate the applicability of two Deep Reinforcement Learning based algorithms for allowing a fine-grained selection of mobility triggers that may instantiate slice and resource mobility actions. While the first proposed algorithm relies on a value-based learning method, the second one exploits a hybrid approach to optimize the action selection process. We present an enhanced ETSI Network Function Virtualization edge computing architecture that incorporates the studied mechanisms to implement service and slice migration. We evaluate the proposed methods' efficiency in a simulated environment and compare their performance in terms of training stability, learning time, and scalability. Finally, we identify and quantify the applicability aspects of the respective approaches.

中文翻译：

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在网络切片移动性中使用强化学习进行触发器选择


最近的 5G 试验证明了网络切片概念的实用性，该概念可以为新的和服务不足的行业领域提供可定制的服务。然而，用户移动性对切片内和切片间最优资源分配的影响值得更多关注。应在使用服务的地方提供切片及其专用资源，以最大限度地减少网络延迟以及相关的开销和成本。不同的移动模式会导致不同的资源重新分配触发，最终在需要迁移足够的资源时导致切片移动。由于触发器的多重性和重叠性质，为资源重新分配和相关切片移动模式选择适当的触发器具有挑战性。在本文中，我们研究了两种基于深度强化学习的算法的适用性，以允许细粒度选择可以实例化切片和资源移动操作的移动触发器。第一个提出的算法依赖于基于价值的学习方法，而第二个算法则利用混合方法来优化动作选择过程。我们提出了一种增强的 ETSI 网络功能虚拟化边缘计算架构，该架构结合了所研究的机制来实现服务和切片迁移。我们评估了所提出的方法在模拟环境中的效率，并比较了它们在训练稳定性、学习时间和可扩展性方面的性能。最后，我们确定并量化了各个方法的适用性。