Wireless and Service Allocation for Mobile Computation Offloading with Task Deadlines

TL;DR

This paper proposes a method for allocating wireless bandwidth and edge server capacity in mobile computation offloading to minimize power consumption while satisfying task deadlines, using convex decomposition for efficient solutions.

Contribution

It introduces a novel approach to jointly optimize wireless and server resources for offloading with deadline constraints, employing convex decomposition of MINLPs.

Findings

Solutions achieve near-optimal performance across various system parameters.

The proposed method effectively balances power consumption and deadline satisfaction.

Convex decomposition enables efficient computation of resource allocations.

Abstract

In mobile computation offloading (MCO), mobile devices (MDs) can choose to either execute tasks locally or to have them executed on a remote edge server (ES). This paper addresses the problem of assigning both the wireless communication bandwidth needed, along with the ES capacity that is used for the task execution, so that task completion time constraints are satisfied. The objective is to obtain these allocations so that the average power consumption of the mobile devices is minimized, subject to a cost budget constraint. The paper includes contributions for both soft and hard task completion deadline constraints. The problems are first formulated as mixed integer nonlinear programs (MINLPs). Approximate solutions are then obtained by decomposing the problems into a collection of convex subproblems that can be efficiently solved. Results are presented that demonstrate the quality of the proposed solutions, which can achieve near optimum performance over a wide range of system parameters.