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NFV-Based Architecture for the Interworking Between WebRTC and IMS

机译:WebRTC和IMS互通的基于NFV的体系结构

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The emerging paradigm of network function virtualization (NFV) technology promises an efficient solution for optimized service deployment in the cloud computing environment thanks to its ability to dynamically add or remove virtual resources when there is a change in workload. Nevertheless, telecom providers are still facing a challenging issue in efficiently adopting NFV to deploy Web real-time communication (WebRTC) service on top of IP multimedia subsystem (IMS). Providing WebRTC service increases the inherent complexity of the IMS system in terms of the number of service nodes as virtual network functions (VNFs) and the way they interact, both of which play significant roles in the problem of optimally allocating resources. This paper proposes a virtualized interworking system between IMS and WebRTC called NFV-based interworking architecture, and describes the mechanism for VNFs to exchange messages with each other. We present an analytic system model considering the constraints of resources, quality of service (QoS), and service costs. A real-time Markov approximation-based resource allocation algorithm (RIDRA) is then designed allowing a provisioned resource at service nodes to be reconfigured in time to meet performance requirements. The proposed solution is evaluated on the large scale by simulation and on the small scale by our developed testbed. Experimental results reveal that our algorithm effectively responds to fluctuating service demands with a service cost reduced by 19% via efficiently allocating virtual resources while maintaining QoS requirement.
机译:网络功能虚拟化(NFV)技术的新兴范例为云计算环境中的优化服务部署提供了一种有效的解决方案,因为它具有在工作负载发生变化时动态添加或删除虚拟资源的能力。尽管如此,在有效采用NFV在IP多媒体子系统(IMS)之上部署Web实时通信(WebRTC)服务方面,电信提供商仍面临挑战。提供WebRTC服务就虚拟网络功能(VNF)的服务节点数量及其交互方式而言,增加了IMS系统的固有复杂性,这两者在优化分配资源的问题上都起着重要作用。本文提出了一种IMS和WebRTC之间的虚拟化互通系统,称为基于NFV的互通体系结构,并描述了VNF相互交换消息的机制。我们提出了一种分析系统模型,其中考虑了资源,服务质量(QoS)和服务成本的约束。然后,设计了一种基于实时马尔可夫近似的资源分配算法(RIDRA),允许及时重新配置服务节点处的预配资源以满足性能要求。通过模拟对提出的解决方案进行大规模评估,而通过我们开发的测试平台对提出的解决方案进行小规模评估。实验结果表明,我们的算法通过有效分配虚拟资源同时保持QoS要求,有效地应对波动的服务需求,服务成本降低了19%。

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