This study explored the distribution of three types of English formulaic language, which involves four categories in L1 Chinese L2 English learners’ speaking performance. In addition, it investigated the relationship between the English learners’ use of formulaic language and their spoken English fluency. A CCA (canonical correlation analysis) was conducted to examine the correlations between two sets of fluency variables (dependent variables) and linguistic variables of English formulaic language use (independent variables). The fluency variable set consists of: (1) temporal indices such as SR (speech rate), AR (articulation rate), MLR (mean length of run), and PTR (phonation time ratio); (2) linguistic variables of English formulaic language like F2R (two-word formulaic sequences/run ratio, B3R (three-word lexical bundles/run ratio), and B4R (four-word lexical bundles/run ratio). These are calculated according to the frequency of the English formulaic language in the speech samples of the participants (n = 86) across three academic levels. The results indicate that the learners’ spoken English fluency is highly related to their use of English formulaic language. Its limitations and future research directions are also discussed.

Network function virtualization (NFV) has the potential to lead to significant reductions in capital expenditure and can improve the flexibility of the network. Virtual network function (VNF) deployment problem will be one of key problems that need to be addressed in NFV. To solve the problem of routing and VNF deployment, an optimization model, which minimizes the maximum index of used frequency slots, the number of used frequency slots, and the number of initialized VNF, is established. In this optimization model, the dependency among the different VNFs is considered. In order to solve the service chain mapping problem of high dynamic virtual network, a new virtual network function service chain mapping algorithm PDQN-VNFSC was proposed by combining prediction algorithm and DQN (Deep Q-Network). Firstly, the real-time mapping of virtual network service chains is modeled into a partial observable Markov decision process. Then, the real-time mapping process of virtual network service chain is optimized by using global and long-term benefits. Finally, the service chain of virtual network function is mapped through the learning decision framework of offline learning and online deployment. The simulation results show that, compared with the existing algorithms, the proposed algorithm has a lower the maximum index of used frequency slots, the number of used frequency slots, and the number of initialized VNF.