An intelligent collaborative inference approach of service partitioning and task offloading for deep learning based service in mobile edge computing networks

摘要

As the rapid evolution of smart devices and real-time applications, many new kinds of computation-intensive services have been emerged successively and the corresponding requirements have been growing dramatically. Extended from cloud computing, mobile edge computing (MEC) is a novel technology which can provide powerful computing resource at the proximity of resource-restrained mobile devices. Thus, it enables collaboration between edge server and mobile device, which can improve the quality of experience for users. In this article, we propose an intelligent collaborative inference (ICI) approach for real-time computation-intensive services in MEC network, which can achieve intelligent service partitioning and partial task offloading. Since machine learning algorithms have been applied in many applications with the advancement of big data and computing power, we focus on the services based on deep-learning. Particularly, we research a service based on Pose-Net model to achieve human pose estimation in the field of computer vision. And we design relevant ICI algorithm to achieve fine-grained video stream processing in consideration of video service requirement, deep neural network (DNN) model structure, mobile device capability, wireless network condition, and cooperative server workload. Based on Python programming language and TensorFlow library, we test the ICI approach with some practical simulation parameters on real hardware platforms. The experiment results show that the presented ICI approach have superior performance in terms of service frame rate and client energy consumption than other benchmark approaches.