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Network function virtualization (NFV) and mobile edge computing (MEC) are two of the promising technologies that are expected to play a critical role in mobile edge cloud networks, thus satisfying ambitious quality of experience (QoE) requirements of the Internet of things (IoT) applications. In MEC-NFV system, a service function chain (SFC) consists of an ordered set of virtual network functions (VNFs) that are connected based on the business logic of service providers. However, the inefficiency of the SFC orchestration process is one major problem due to the dynamic nature of mobile edge cloud networks and abundance of IoT terminals. In this letter, a quantum machine learning (QML)-based scheme is proposed as solution that can handle complex and dynamic SFC orchestration in mobile edge cloud networks. Simulation results show that our proposal significantly provides more than 8-fold reduction of run time compared to the Viterbi algorithm, and the end-to-end delay is only about 1.1 times of the exact solution.

In order to deal with the rapid growth of mobile data traffic and reduce the infrastructure capital expenditure (CAPEX) and operating expense (OPEX), C-RAN is introduced into wireless access network architecture. C-RAN can implement the separation of baseband processing unit (BBU) and remote radio head (RRH). Baseband processing resources are shared by the centralizing deployment of BBU in data center, while RRH is only as a wireless access point that is responsible for sending and receiving signals. Software Defined Network (SDN) and Network Function Virtualization (NFV), the candidate technologies for future development of wireless access network, can promote the virtualization of wireless access network. In this paper, the dynamic allocation of carrier resource in BBU pool is studied, and a dynamic combination allocation algorithm based on Karnaugh map and enhance genetic algorithm (KMEG) for resource allocation of carrier resource is also presented. The simulation results show that this algorithm can deal with different mobile service request for underlying resource effectively, which can adjust system capacity dynamically on demand and achieve the dynamic share of heterogeneous underlying resources in BBU pool. In addition, this algorithm can improve the receiving rate of VNs compared with the static resource allocation algorithm, which can enhance the utilization rate of underlying resources and spectrum efficiency greatly.