Task Offloading and Resource Allocation for Edge-Enabled Mobile Learning

Mobile learning has evolved into a new format of education based on communication and computer technology that is favored by an increas-ing number of learning users thanks to the devel-opment of wireless communication networks, mobile edge computing, artificial intelligence, and mobile de-vices. However, due to the constrained data process-ing capacity of mobile devices, efficient and effective interactive mobile learning is a challenge. Therefore, for mobile learning, we propose a "Cloud, Edge and End" fusion system architecture. Through task of-floading and resource allocation for edge-enabled mo-bile learning to reduce the time and energy consump-tion of user equipment. Then, we present the proposed solutions that uses the minimum cost maximum flow (MCMF) algorithm to deal with the offloading prob-lem and the deep Q network (DQN) algorithm to deal with the resource allocation problem respectively. Fi-nally, the performance evaluation shows that the pro-posed offloading and resource allocation scheme can improve system performance, save energy, and satisfy the needs of learning users.