Stackelberg-Game-Based Multi-User Multi-Task Offloading in Mobile Edge Computing

Abstract

Mobile edge computing (MEC) brings abundant computing resources to the edge networks, which supports users in offloading their tasks to the edge instead of the cloud, thereby reducing service delay and improving users' quality of experience. In this paper, we consider a three-tier multi-user multi-task offloading model, which contains multiple users with each user possessing multiple tasks, multiple base stations (BSs) with edge servers and a remote cloud. Taking into account the selfishness of individuals in the MEC system, we respectively formulate optimization problems for users, BSs and the cloud. Users aim to make their offloading strategies to minimize their respective costs, while BSs and the cloud aim to make their computation resource allocation decisions to minimize their respective task completion delays. We model the interaction among these selfish individuals based on Stackelberg game, where users act as leaders and BSs and the cloud act as followers. By using backward induction, we prove the existence of Stackelberg Equilibrium (SE). We further propose a distributed algorithm that enables the system to reach the SE, which includes three user selection strategies for the BSs. The numerical results demonstrate the superiority of the proposed scheme compared with several approaches.