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A cloud native solution for dynamic auto scaling of MME in LTE

机译:用于LTE中MME动态自动缩放的云原生解决方案

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Due to rapid growth in the use of mobile devices and as a vital carrier of IoT traffic, mobile networks need to undergo infrastructure wide revisions to meet explosive traffic demand. In addition to data traffic, there has been a significant rise in the control signaling overhead due to dense deployment of small cells and IoT devices. Adoption of technologies like cloud computing, Software Defined Networking (SDN) and Network Functions Virtualization (NFV) is impressively successful in mitigating the existing challenges and driving the path towards 5G evolution. However, issues pertaining to scalability, ease of use, service resiliency, and high availability need considerable study for successful roll out of production grade 5G solutions in cloud. In this work, we propose a scalable Cloud Native Solution for Mobility Management Entity (CNS-MME) of mobile core in a production data center based on micro service architecture. The micro services are lightweight MME functionalities, in contrast to monolithic MME in Long Term Evolution (LTE). The proposed architecture is highly available and supports auto-scaling to dynamically scale-up and scale-down required micro services for load balancing. The performance of proposed CNS-MME architecture is evaluated against monolithic MME in terms of scalability, auto scaling of the service, resource utilization of MME, and efficient load balancing features. We observed that, compared to monolithic MME architecture, CNS-MME provides 7% higher MME throughput and also reduces the processing resource consumption by 26%.
机译:由于移动设备使用的快速增长以及作为物联网流量的重要载体,移动网络需要进行基础架构范围的修订,以满足爆炸性的流量需求。除数据流量外,由于小型小区和物联网设备的密集部署,控制信令开销也显着增加。采用云计算,软件定义网络(SDN)和网络功能虚拟化(NFV)等技术在缓解现有挑战和推动5G演进的道路上取得了令人瞩目的成功。但是,有关可伸缩性,易用性,服务弹性和高可用性的问题需要大量研究才能成功在云中成功推出生产级5G解决方案。在这项工作中,我们提出了一种基于微服务架构的生产数据中心中移动核心移动管理实体(CNS-MME)的可扩展云原生解决方案。与长期演进(LTE)中的单片MME相比,微服务是轻量级MME功能。所提出的体系结构是高度可用的,并且支持自动扩展以动态地扩展和缩减所需的微服务以实现负载平衡。针对可扩展性,服务的自动伸缩,MME的资源利用率以及有效的负载平衡功能,针对整体MME评估了所提出的CNS-MME体系结构的性能。我们观察到,与单片MME体系结构相比,CNS-MME提供了高7%的MME吞吐量,还减少了26%的处理资源消耗。

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