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A Scaling Mechanism for an Evolved Packet Core Based on Network Functions Virtualization

机译:基于网络功能虚拟化的演化分组核心的缩放机制

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摘要

The workload variations affect the performance of mobile networks. The scaling task is pivotal for addressing these variations. In the literature, research works have incorporated horizontal or vertical scaling in the virtualized network functions of the Evolved Packet Core (EPC) to improve its performance. However, up to now, these works exploit only horizontal or vertical scaling for achieving their aim. In this paper, we propose a scaling mechanism that utilizes horizontal and vertical scaling and considers workload variations for improving performance in EPC. This mechanism is threshold-based, straightforward, and implementable in real LTE-EPC scenarios. We also develop a mechanism prototype and deploy it in a real public cloud. In this cloud, we conduct a prototype evaluation, regarding registrations per second, latency, CPU, and RAM, and considering a varying workload. The evaluation results reveal that our mechanism increases the registrations per second about 308% and decreases the corresponding latency approximately 70% regarding an EPC without scaling while keeping the CPU usage lower than 90% and the used capacity of registrations per second between 65% and 90%. These results corroborate the importance of used both horizontal and vertical scaling to improve EPC performance, handle workload variations, and save resources.
机译:工作量变化会影响移动网络的性能。缩放任务是用于解决这些变化的关键。在文献中,研究工作已经在演进分组核心(EPC)的虚拟化网络功能中纳入了水平或垂直缩放,以提高其性能。但是,到目前为止,这些作品仅利用水平或垂直缩放来实现其目标。在本文中,我们提出了一种利用水平和垂直缩放的缩放机制,并考虑改善EPC中性能的工作量变化。该机制是基于阈值的,直接的,并且可以在Real LTE-EPC场景中实现。我们还开发一个机制原型并将其部署在真正的公共云中。在这一云中,我们对每个秒,延迟,CPU和RAM的注册进行了一个原型评估,并考虑了不同的工作量。评价结果表明,我们的机制每秒增加约308%的注册,并降低约70%的关于EPC而不缩放的相应延迟,同时保持低于90%的CPU使用率,每秒注册的使用能力在65%和90之间。 %。这些结果证实了使用水平和垂直缩放的重要性,以改善EPC性能,处理工作量变化和节省资源。

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