Min-Max Latency Optimization for Multiuser Computation Offloading in Fog-Radio Access Networks

Abstract

This paper considers mobile computation offloading in fog-radio access networks (F-RAN), where multiple mobile users offload their computation tasks to the F-RAN through a number of fog nodes [a.k.a. enhanced remote radio heads (RRHs)]. In addition to communication capability, the fog nodes are also equipped with computational resources to provide computing services for users. Each user chooses one fog node to offload its task, while each fog node may simultaneously serve multiple users. Depending on computational burden at the fog nodes, the tasks may be completed at the fog nodes or further offloaded to the cloud via fronthaul links with limited capacities. To complete all the tasks as fast as possible, a joint optimization of radio and computational resources of F-RAN is proposed to minimize the maximum latency of all users. This problem is formulated as a mixed integer nonlinear program (MINP). We first show that the MINP can be reformulated as a continuous optimization problem with a difference-of-convex (DC) objective. Then, an inexact DC algorithm is proposed to handle the min-max problem with stationary convergence guarantee. Simulation results show that the proposed algorithm outperforms the minimum distance-based and the random-based offloading strategies.