Joint Task Assignment, Transmission, and Computing Resource Allocation in Multilayer Mobile Edge Computing Systems

In this paper, we propose a multilayer data flow processing system, i.e., EdgeFlow, to integrally utilize the computing capacity throughout the whole network, i.e., the cloud center (CC) on the top layer, the mobile edge computing (MEC) servers on the middle layer, and the edge devices (EDs) on the bottom layer. To realize the efficient data processing in EdgeFlow, we optimally assign the tasks to multiple layers, and allocate the wireless transmission resources between the MEC servers and EDs as well as the wired transmission resources between the CC and MEC servers. We prove that the system is naturally classified into two states, the nonblocking state and the blocking state, according to various data generation speed at the EDs. The system latency is minimized for the nonblocking state even though the problem is nonconvex. As for the blocking state, the recovery time is minimized through solving a min-max problem. Based on the analytical results, the EdgeFlow system is implemented on the universal software radio peripheral and the Intel next units of computing. A typical Internet of Things application, photo recording and face recognition, is used for the simulation and the experiment, and indicates that the EdgeFlow can achieve a low latency and recovery time than the previous distributed frameworks, e.g., the Cloudlet and the Markov decision process.