论文标题

IP多媒体子系统在软木化环境中的统计评估:排队网络方法

Statistical Assessment of IP Multimedia Subsystem in a Softwarized Environment: a Queueing Networks Approach

论文作者

Di Mauro, Mario, Liotta, Antonio

论文摘要

下一代5G网络可以大大受益于虚拟化范式之间的协同作用,例如网络函数虚拟化(NFV)和服务提供平台,例如IP Multimedia子系统(IMS)。 NFV概念正在朝着基于容器的轻质解决方案发展,与经典虚拟机相比,该解决方案不会携带整个操作系统,并导致更有效,更可扩展的部署。另一方面,例如,IMS已成为5G核心网络不可或缺的一部分,以提供高级服务,例如LETE(VOLTE)。在本文中,我们结合了这些虚拟化和服务供应概念,得出了被称为CIMS的集装箱IMS基础架构,并通过统计表征和实验测量来提供评估。具体而言,我们:i)通过排队网络方法模型CIMS模型,以表征在受约束条件下虚拟资源的利用; ii)绘制Pollaczek-khinchin公式的扩展版本,这对于处理散装到达非常有用; iii)提供了一个优化问题,重点是在有能力限制的情况下最大化整个CIMS绩效,从而为服务提供商管理服务水平协议(SLA)提供了新的手段; $ iv)$评估一系列CIMS方案,考虑了包括多个工作类别在内的不同排队学科。基于开源平台Clearwater的实验测试台已部署,以得出关键参数的一些现实值(例如到达和服务时间)。

The Next Generation 5G Networks can greatly benefit from the synergy between virtualization paradigms, such as the Network Function Virtualization (NFV), and service provisioning platforms such as the IP Multimedia Subsystem (IMS). The NFV concept is evolving towards a lightweight solution based on containers that, by contrast to classic virtual machines, do not carry a whole operating system and result in more efficient and scalable deployments. On the other hand, IMS has become an integral part of the 5G core network, for instance, to provide advanced services like Voice over LTE (VoLTE). In this paper we combine these virtualization and service provisioning concepts, deriving a containerized IMS infrastructure, dubbed cIMS, providing its assessment through statistical characterization and experimental measurements. Specifically, we: i) model cIMS through the queueing networks methodology to characterize the utilization of virtual resources under constrained conditions; ii) draw an extended version of the Pollaczek-Khinchin formula, which is useful to deal with bulk arrivals; iii) afford an optimization problem focused at maximizing the whole cIMS performance in the presence of capacity constraints, thus providing new means for the service provider to manage service level agreements (SLAs); $iv)$ evaluate a range of cIMS scenarios, considering different queuing disciplines including also multiple job classes. An experimental testbed based on the open source platform Clearwater has been deployed to derive some realistic values of key parameters (e.g. arrival and service times).

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