Most researches of emotion recognition focus on single person information. However, everyone's emotions will affect each other. For example, when the teacher is angry, the student's nervousness will increase. But the facial expression information of the light single is already quite large. It means that group emotion recognition will encounter a huge traffic bottleneck. Therefore, there is a vast amount of data collected by end-devices that will be uploaded to the emotion cloud for big data analysis. Because different emotions may require different analytical methods, in the face of diverse big data, connecting different emotion clouds is a viable alternative method to extend the emotion cloud hardware. In this paper, we built a software defined networking (SDN) multi-emotion cloud platform to connect different emotion clouds. Through the advantages of the splicing control plane and the data plane, the routing path can be changed using software. This means that the individual conditions of different students can be handled by a dedicated system via service function (SF). The load balancing effect between different emotion clouds is achieved by optimizing the SFC. In addition, we propose a SFC-based dynamic load balancing mechanism which eliminates a large number of SFC creation processes. The simulation results show that the proposed mechanism can effectively allocate resources to different emotion clouds to achieve real-time emotion recognition. This is the first strategy to use SFC to balance the emotion data that the teachers can change teaching policy in a timely manner in response to students' emotions.
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