An Incentive-Aware Job Offloading Control Framework for Mobile Edge Computing

TL;DR

This paper develops a novel utility-based framework for incentivizing mobile users to offload computation to edge servers, incorporating physical layer parameters and analyzing equilibrium states for optimal pricing strategies.

Contribution

It introduces a new utility function dependent on physical parameters and derives closed-form Nash and Social Equilibria for incentive-aware offloading games.

Findings

Closed-form Nash and Social Equilibria derived.

Pricing scheme converges to socially optimal equilibrium.

Utility function incorporates physical layer parameters.

Abstract

This paper considers a scenario in which an access point (AP) is equipped with a mobile edge server of finite computing power, and serves multiple resource-hungry mobile users by charging users a price. Pricing provides users with incentives in offloading. However, existing works on pricing are based on abstract concave utility functions (e.g, the logarithm function), giving no dependence on physical layer parameters. To that end, we first introduce a novel utility function, which measures the cost reduction by offloading as compared with executing jobs locally. Based on this utility function we then formulate two offloading games, with one maximizing individual's interest and the other maximizing the overall system's interest. We analyze the structural property of the games and admit in closed form the Nash Equilibrium and the Social Equilibrium, respectively. The proposed expressions are functions of the user parameters such as the weights of computational time and energy, the distance from the AP, thus constituting an advancement over prior economic works that have considered only abstract functions. Finally, we propose an optimal pricing-based scheme, with which we prove that the interactive decision-making process with self-interested users converges to a Nash Equilibrium point equal to the Social Equilibrium point.