Edge Intelligence empowered Social Image Recognition using Microservices Architecture

With the emergence of diversified mobile applications in the social field, e.g., image recognition, the massive growth in mobile data has imposing more service requests with strict real-time requirement on servers, therefore, the edge computing as an effective paradigm has been used to provide low-latency intelligence services. To improve the efficiency of concurrent processing on the edge server, the microservices architecture also has been adopted to offer a promising data processing solution. In this paper, through combining the stated advantages for social image recognition, an edge intelligence empowered scheme using microservices architecture is proposed. To enhance the efficiency of model training for image recognition, an optimized training model within the native framework of convolutional neural network (CNN) is firstly proposed. And then, to achieve the effective and low-latency social image recognition, a two-layer edge-end architecture is designed, within which, the image classification model is divided into feature extractor and image classifier, which are respectively deployed to the edge server and mobile terminals. Using the partial offloading, the mobile terminals perform the image preprocessing and classification, while the image feature extraction is executed on the edge server. In the experiments, the efficiency of the optimized training model is verified through comparing the training loss and accuracy, and the efficiency of the designed edge-end architecture is demonstrated with the concurrent testing.