利用基于软件的网络重新设计蜂窝基础设施的考虑

Ali Mohammadkhan, K. Ramakrishnan, A. S. Rajan, C. Maciocco
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引用次数: 8

摘要

随着对无线移动连接的需求持续爆炸式增长,蜂窝网络基础设施容量需求持续增长。虽然5G试图解决无线电层的容量需求,但蜂窝核心网络基础设施(称为增强型分组核心(EPC))上的负载强调了网络基础设施。我们的工作考察了当前蜂窝基础设施的架构、协议和EPC的工作量。我们研究了尺寸容量方面的挑战,并审查了设计替代方案,以支持所需的显著扩展,甚至是在不久的将来。我们将网络基础设施上的工作负载分解为其组件——信令事件事务;数据库或查找事务和包处理。我们定量地展示了EPC各个组件上的控制平面和数据平面负载,并估计了未来5G蜂窝网络工作负载将如何扩展。这一分析有助于我们了解未来5G EPC网络组件的可扩展性挑战。其他扩展5G蜂窝网络的努力采用系统视图,其中控制平面与数据路径分离,并在集中式SDN控制器上终止。SDN控制器在广泛分布的交换基础设施上配置数据路径。我们对工作量的分析告诉我们各种设计方案的可行性,并激励我们努力开发我们的清洁方法,称为CleanG。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Considerations for re-designing the cellular infrastructure exploiting software-based networks
As demand for wireless mobile connectivity continues to explode, cellular network infrastructure capacity requirements continue to grow. While 5G tries to address capacity requirements at the radio layer, the load on the cellular core network infrastructure (called Enhanced Packet Core (EPC)) stresses the network infrastructure. Our work examines the architecture, protocols of current cellular infrastructures and the workload on the EPC. We study the challenges in dimensioning capacity and review the design alternatives to support the significant scale up desired, even for the near future. We breakdown the workload on the network infrastructure into its components-signaling event transactions; database or lookup transactions and packet processing. We quantitatively show the control plane and data plane load on the various components of the EPC and estimate how future 5G cellular network workloads will scale. This analysis helps us to understand the scalability challenges for future 5G EPC network components. Other efforts to scale the 5G cellular network take a system view where the control plane is separated from the data path and is terminated on a centralized SDN controller. The SDN controller configures the data path on a widely distributed switching infrastructure. Our analysis of the workload informs us on the feasibility of various design alternatives and motivates our efforts to develop our clean-slate approach, called CleanG.
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