Latency Minimization for Multiuser Computation Offloading in Fog-Radio Access Networks

TL;DR

This paper develops an optimization framework for minimizing latency in multiuser fog-radio access networks by jointly optimizing offloading, association, and resource allocation, employing an inexact majorization minimization approach.

Contribution

It introduces an inexact MM algorithm for joint latency minimization in F-RAN, applicable to both cooperative and non-cooperative offloading models, with proven convergence guarantees.

Findings

Proposed algorithms outperform heuristic strategies.

Cooperative offloading achieves lower latency.

Inexact MM reduces computational complexity.

Abstract

This paper considers computation offloading in fog-radio access networks (F-RAN), where multiple user equipments (UEs) offload their computation tasks to the F-RAN through a number of fog nodes. Each UE can choose one of the fog nodes to offload its task, and each fog node may serve multiple UEs. Depending on the computation burden at the fog nodes, the tasks may be computed by the fog nodes or further offloaded to the cloud via capacity-limited fronthaul links. To compute all UEs' tasks as fast as possible, joint optimization of UE-Fog association, radio and computation resources of F-RAN is proposed to minimize the maximum latency of all UEs. This min-max problem is formulated as a mixed integer nonlinear program (MINP). We first show that the MINP can be reformulated as a continuous optimization problem, and then employ the majorization minimization (MM) approach to find a solution. The MM approach that we develop is unconventional in that -- each MM subproblem can be solved inexactly with the same provable convergence guarantee as the conventional exact MM, thereby reducing the complexity of each MM iteration. In addition, we also consider a cooperative offloading model, where the fog nodes compress-and-forward their received signals to the cloud. Under this model, a similar min-max latency optimization problem is formulated and tackled again by the inexact MM approach. Simulation results show that the proposed algorithms outperform some heuristic offloading strategies, and that the cooperative offloading can better exploit the transmission diversity to attain better latency performance than the non-cooperative one.