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Flexible Mobile Network Service Chaining in an NFV Environment: IMS Use Case

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Lecture Notes in Real-Time Intelligent Systems (RTIS 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 756))

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Abstract

With the emergence of the software defined network (SDN) and network functions virtualization (NFV) initiatives, mobile network operators (MNOs) look to make a gain from these leading technologies to mitigate hardware costs and reduce time to market. Especially with the use of NFV models, the MNO looks for scaling services up and down fast and also to reduce costs to align with network usage. Those procedures are conducted through flexible service chaining through implemented SDN controller functions along with NFV orchestration modules. In this paper, we give an introduction to a virtualized, flexible mobile network service chaining approach in an NFV environment, as an extended architecture of the 4G/LTE system defined by the 3GPP in order to draw a new design in favor of the 5G network, more especially from an Internet Protocol (IP) and Session Initiation Protocol (SIP) based network point of view. In this paper, we present our approach for dynamic and static service chaining. Moreover, as the IP Multimedia Subsystem (IMS) is one of the considerable use cases for NFV, we set up a testbed system with the goal of evaluating the behavior of a virtualized IMS core network, as a part of operator-based services, within a static service provisioning context. Experiment results show that the IMS system performance could be enhanced quickly in the case of unforeseen degradation by supporting a new VNF based service function path. In addition, SIP key performance indicators get enhanced in terms of registration delay and mean.

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Correspondence to Youssef Seraoui .

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Seraoui, Y., Belmekki, M., Bellafkih, M., Raouyane, B. (2019). Flexible Mobile Network Service Chaining in an NFV Environment: IMS Use Case. In: Mizera-Pietraszko, J., Pichappan, P., Mohamed, L. (eds) Lecture Notes in Real-Time Intelligent Systems. RTIS 2017. Advances in Intelligent Systems and Computing, vol 756. Springer, Cham. https://doi.org/10.1007/978-3-319-91337-7_27

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