论文标题
虚拟化被动光网络的最佳切片,以支持云范围和多访问边缘计算的密集部署
Optimal Slicing of Virtualised Passive Optical Networks to Support Dense Deployment of Cloud-RAN and Multi-Access Edge Computing
论文作者
论文摘要
通过提供较低的成本和更敏捷的小细胞部署,云型摩擦和开放量的商业化,尤其是开放式RAN是实现5G细胞致密化的关键因素。此外,MEC的采用对于支持关键任务应用所需的超低潜伏期和高可靠性很重要,这构成了5G的里程碑,并且超出了完全连接的社会的愿景。但是,以低成本连接天线位点,C-RAN处理和MEC是具有挑战性的,因为它需要高容量,低潜伏期的连接性通过高度相互连接的拓扑提供。当PON被视为向C-RAN提供低成本连接性的解决方案时,它们仅允许数据传输从端点(例如在天线站点托管RU)向中央节点(例如,中央办公室,托管计算设备),因此无法支持从RU向MEC End Nodes提供的流量,这些交通可能会托管DU和可能的CU和网络核心。这导致了对PON体系结构的演变的研究,并能够在端点之间提供直接通信,从而支持MEC安装所需的网格流量模式。在这种情况下,根据其通信模式,虚拟化在将有效的资源分配(即光传输容量)启用到端点方面起着关键作用。在本文中,我们解决了虚拟PON切片在网状杆架构上的动态分配的挑战,以支持C-RAN和MEC节点。我们利用混合的分析词模型来计算最佳的虚拟PON切片分配,目的是最大程度地减少MEC节点资源的使用,同时达到目标延迟阈值(在我们的情况下为100 $μs$)。我们的方法在减少计算时间时特别有效,可以使虚拟PON切片分配与实时或接近实时操作兼容的时间表。
The commercialization of Cloud-RAN, and Open-RAN in particular, is a key factor to enable 5G cell densification, by providing lower cost and more agile deployment of small cells. In addition, the adoption of MEC is important to support ultra-low latency and high reliability required by mission-critical applications, which constitute a milestone of the 5G and beyond vision of a fully connected society. However, connecting antenna site, C-RAN processing and MEC at low cost is challenging, as it requires high-capacity, low latency connectivity delivered through a highly inter-connected topology. While PON is being considered as a solution for providing low-cost connectivity to C-RAN, they only allow data transmission from the endpoints (for example hosting RU at the antenna site) towards a central node (e.g., the central office, hosting computing equipment), thus cannot support traffic from RU towards MEC end nodes that could host DU and possibly CU and network core. This led to research into the evolution of PON architectures with the ability to provide direct communications between endpoints, thus supporting mesh traffic patterns required by MEC installations. In this context, virtualization plays a key role in enabling efficient resource allocation (i.e. optical transmission capacity) to endpoints, according to their communication patterns. In this article, we address the challenge of dynamic allocation of virtual PON slices over mesh-PON architectures to support C-RAN and MEC nodes. We make use of a mixed analytical-iterative model to compute optimal virtual PON slice allocation, with the objective of minimizing the use of MEC node resources, while meeting a target latency threshold (100 $μs$ in our scenario). Our method is particularly effective in reducing computation time, enabling virtual PON slice allocation in timescales compatible with real-time or near real-time operations.